Vasant Honavar

Artificial Intelligence Research Laboratory

Index

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Books

Honavar, V. and Lin, H. (2020). Learning Predictive Models from Linked Open Data. In preparation.
Santhanam, G., Basu, S., and Honavar, V. (2016). Representing and Reasoning About Qualitative Preferences. Synthesis Lectures in Artificial Intelligence and Machine Learning. Morgan and Claypool.
Honavar, V., Zhang, J., Caragea, C., Bui, N. (2020) Learning Predictive Models from Sparse, Partially Specified Data. In preparation.
Honavar, V., Caragea, D., Koul, N., Silvescu, A. (2020). Learning Predictive Models from Big Data Using Statistical Queries. In preparation.
Honavar, V. and Slutzki, G. (1998) (Ed.). Proceedings of the Fourth International Colloquium on Grammatical Inference. (LNCS Vol. 1433). Berlin: Springer-Verlag.
Patel, M., Honavar, V., and Balakrishnan, K. (2001) (Ed.) Evolutionary Synthesis of Intelligent Agents. Boston, MA: MIT Press.
Honavar, V. and Uhr, L. (1994) (Ed). Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. New York, NY: Academic Press.

Refereed Journal and Conference Publications and Selected White Papers

Liang, J., Ren, W., Hanifi, S. and Honavar, V. (2024). Inducing Clusters Deep Kernel Gaussian Process for Longitudinal Data. In: Proceedings of the AAAI Conference on Artificial Intelligence.

Dalvi, A., Ashtekar, N., and Honavar V. (2024). Causal Effect Estimation Using Random Hyperplane Tessellations. In: Proceedings of the 3rd Conference on Causal Learning and Reasoning.

Ren, W. and Honavar, V. (2024). EsaCL: An Efficient Continual Learning Algorithm. In Proceedings of the SIAM Conference on Data Mining.

Basu, S., Honavar, V., Santhanam, G., and Tao, J. (2023). Representing and Reasoning with Multi-Stakeholder Qualitative Preference Queries. In: Proceedings of the European Conference on Artificial Intelligence (ECAI-2023).

Roberts, D.M., Schade, M.M., Master, L., Honavar, V.G., Nahmod, N.G., Chang, A.M., Gartenberg, D., and Buxton, O.M. (2023). Performance of an open machine learning model to classify sleep/wake from actigraphy across∼ 24-hour intervals without knowledge of rest timing. Sleep health, 9(5), pp.596-610.
Adishesha, A.S., Jakielaszek, L., Azhar, F., Zhang, P., Honavar, V., Ma, F., Belani, C., Mitra, P. and Huang, S.X. (2023). Forecasting User Interests Through Topic Tag Predictions in Online Health Communities. IEEE Journal of Biomedical and Health Informatics.

Ashtekar, N., and Honavar, V. (2023). A Simple, Fast Algorithm for Continual Learning from High-Dimensional Data. In: Proceedings of International Conference on Representation Learning (ICLR 2023).

Gulhan, A., Akbulut, G., Amritkar, A., Sampson, J. Honavar, V., Focht, A., Pavlosvki C., Kandemir, M. (2023). License Forecasting and Scheduling for HPC. In: 31st International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems.

Looti, AL, Petucci, J., Katoch, A., Honavar, V. (2023) Machine Learning Prediction of Seizures after Ischemic Strokes. Neurology, DOI: 10.1212/WNL.0000000000203063 (2023).

Jung, Y., Geng, C., Bonvin, A.M., Xue, L.C. and Honavar, V.G. (2023). MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein–Protein Docking Conformations. Biomolecules, 13(1), p.121.

Schade, M.M., Roberts, D.M., Honavar, V.G. and Buxton, O.M. (2023). Machine learning approaches in sleep and circadian research. In Encyclopedia of Sleep and Circadian Rhythms: Volume 1-6, Second Edition (pp. 53-62). Elsevier.

Dalvi, A., Acharya, A., Gao, J. and Honavar, V.G. (2022) Variational Graph Auto-Encoders for Heterogeneous Information Network. In NeurIPS 2022 Workshop: New Frontiers in Graph Learning.

Kallitsis, M., Prajapati, R., Honavar, V., Wu, D., and Yen, J. (2022) Detecting and Interpreting Changes in Scanning Behavior in Large Network Telescopes. IEEE Transactions on Information Forensics and Security 17 (2022): 3611-3625.

Seto, C. H., Graif, C., Khademi, A., Honavar, V. G., & Kelling, C. E. (2022). Connected in health: Place-to-place commuting networks and COVID-19 spillovers. Health & Place, 77, 102891.

Graif, C., Seto, C. and Honavar, V. (2022). Child Overdoses Amid the Deaths-of-Despair Epidemic: Racial and Ethnic Differences in Intergenerational and Network Diffusion. In: Annual Meeting of the Population Society of America.
Cwiek, A., Rajtmajer, S.M., Wyble, B., Honavar, V., Grossner, E. and Hillary, F.G. (2022). Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics. Network Neuroscience, 6(1), pp.29-48.

Liang, J., Wu, Y., Yu, D., and Honavar, V. (2021). Longitudinal Deep Kernel Gaussian Process Regression In: Proceedings of the 35th AAAI Conference on Artificial Intelligence.
Hsieh, T-Y., Sun, Y., Wang, S., and Honavar, V. (2021). Functional Autoencoders for Functional Data Representation LearningIn: Proceedings of the SIAM Conference on Data Mining.
Hsieh, T-Y., Sun, Y., Tang, X., Wang, S., and Honavar, V. (2021). SrVARM: State Regularized Vector Autoregressive Model forJoint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Time Series DataIn: Proceedings of the Web Conference.
Hsieh, T-Y., Sun, Y., Wang, S.m and Honavar, V. (2021). Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals. In: Proceedings of the 14th International Conference on Web Search and Data Mining.
Liang, J., Guo, W., Luo, T., Honavar, V., Wang, G., and Xing, X. (2021) FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data. In: Proceedings of the Network and Distributed System Security Symposium.
Seto, C., Khademi, A., Graif, C., and Honavar, V. (2021). Commuting Network Spillovers and COVID-19 Deaths Across US Counties. In the Annual Meeting of the Population Society of America.
Gur, S., El-Manzalawy, Y., Diaz, M., and Honavar, V. (2021). Age-related differences in task-specific functional connectivity in phonological and semantic picture-based match-mismatch tasks in the presence of distractor words. Under review.
Le. T. and Honavar, V. (2020). Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data Proceedings of the ACM-IMS Conference on Foundations of Data Science Conference. pp. 183–188. https://doi.org/10.1145/3412815.3416894
Liang, J., Xu, D., Sun, Y., and Honavar, V. (2020). LMLFM: Longitudinal Multi-Level Factorization Machines. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. AAAI 2020: 4811-4818
Khademi, A. and Honavar, V. (2020). Algorithmic Bias in Recidivism Prediction: A Causal Perspective. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. AAAI 2020: 13839-13840
Hou Y, Wu C, Yang D, Ye T, Honavar VG, Van Duin AC, Wang K, Priya S.(2020) Two-dimensional hybrid organic–inorganic perovskites as emergent ferroelectric materials. Journal of Applied Physics 128, https://doi.org/10.1063/5.0016010.
Sun, Y., Wang, S., Tang, X., Hsieh, T-Y., and Honavar, V. (2020).Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach. Proceedings of The Web Conference 2020 (WWW ’20) https://doi.org/10.1145/3366423.3380149
Geng, C., Jung, Y., Renaud, N., Honavar, V., Bonvin, A., Xue, L. (2020). iScore: A novel graph kernel-based function for scoring protein-protein docking models, Bioinformatics. https://doi.org/10.1093/bioinformatics/btz496.
Lee, S. and Honavar, V. (2020). Towards Robust Relational Causal Discovery. In: Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence. pp. 345-355
Parashar, M., Honavar, V., Simonet, A., Rodero, I., Ghahramani, F., Agnew, G., and Jantz, R. (2020). The Virtual Data Collaboratory: A Regional Cyberinfrastructure for Collaborative Data-Driven Research. Computing in Science and Engineering. 22(3): 79-92

Renaud, N., Jung, Y., Honavar, V., Geng, C., Bonvin, A.M. and Xue, L.C., 2020. iScore: An MPI supported software for ranking protein–protein docking models based on a random walk graph kernel and support vector machines. SoftwareX, 11, p.100462.
Kandasamy, S., Bhattacharyya, A., and Honavar, V. (2019). Minimum Intervention Cover of a Causal Graph. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence.
Khademi, A., Lee, S., Foley, D., and Honavar, V. (2019). Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality. In: Proceedings of the Web Conference. pp. 2907-2914.
Sun, Y., Tang, X., Hsieh, T-Y., Wang, S., and Honavar, V. (2019). MEGAN: A Generative Adversarial Network Algorithm for Multi-View Network Embedding. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence.
Yong, J., El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2019). Partner-Specific Prediction of RNA-binding Residues in Proteins: A Critical Assessment. In: Proteins: Structure, Function, and Bioinformatics, https://doi.org/10.1002/prot.25639
Honavar, V. (2019). Machine Learning in clinical care: Quo Vadis? Indian Journal of Ophthalmology. Vol. 67. pp. 985-986.
Hsieh, T-Y, Sun, Y., Wang, S., and Honavar, V. (2019). Adaptive Structural Co-regularization for Unsupervised Multi-view Feature Selection. In: Proceedings of the IEEE International Conference on Big Knowledge.
Abbas, M., Matta, J., Le, T., Obafemi-Ajayi, T., Honavar, V., and El-Manzalawy, Y. (2019). Discovering Inflammatory Bowel Disease Biomarkers Using Gut Microbiome Network Based Feature Selection. PLOS One.
Khademi, A., El-Manzalawy, Y., Master, L., Buxton, O., and Honavar, V. (2019). Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach. Nature and Science of Sleep. https://doi.org/10.2147/NSS.S220716
Liang, J., Hu, J., Dong, S., and Honavar, V. (2018). Top-N-Rank: A Truncated List-wise Ranking Approach for Large-scale Top-N Recommendation. In: Proceedings of the IEEE International Conference on Big Data. pp. 1052-1058
Hsieh, T-Y., El-Manzalawy, Y., Sun, Y., and Honavar, V (2018). Compositional Stochastic Average Gradient for Machine Learning and Related Applications. In: Proceedings of the 19th International Conference on Intelligent Data Engineering and Automated Learning. In press.
Abbas M, Le T, Bensmail H, Honavar V, El-Manzalawy Y (2018). Microbiomarkers Discovery in Inflammatory Bowel Diseases using Network-Based Feature Selection. Proceedings of the 9th ACM Conference on Bioinformatics, Computational Biology and Health Informatics.
El-Manzalawy, Y., Hsieh, T.Y., Shivakumar, M., Kim, D. and Honavar, V. (2018). Min-Redundancy and Max-Relevance Multi-view Feature Selection for Predicting Ovarian Cancer Survival using Multi-omics Data. BMC Medical Genomics.
Hu, J, Liang, J, Kuang, Y, and Honavar, V. (2018). A user similarity-based Top-N recommendation approach for mobile in-application advertising Expert Systems with Applications, vol. 111, pp. 51-60. DOI: 10.1016/j.eswa.2018.02.012
Gur, S., and Honavar, V. (2018). PATENet: Pairwise Alignment of Time Evolving Networks.. in: Proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Computer Science, vol. 10934 LNAI, Springer Verlag, pp. 85-98
Khademi, A., El-Manzalawy, Y., Buxton, O., and Honavar, V. (2018). Toward Personalized Sleep/Wake Prediction from Actigraphy. IEEE International Conference on Biomedical and Health Informatics. pp. 414-417.
Sun, Y., Bui, N., Hsieh, T-Y., and Honavar, V. (2018). Multi-View Network Embedding Via Graph Factorization Clustering and Co-Regularized Multi-View Agreement. In: Proceedings of the Eighth IEEE ICDM Workshop on Data Mining in Networks, IEEE.
Barocas, S., Bradley, E., Honavar, V. and Provost, F. (2017). Big Data, Data Science, and Civil Rights A white paper prepared for the Computing Community Consortium committee of the Computing Research Association. arXiv preprint arxiv:1706.03102.
Lee, S. and Honavar, V. (2017). Self-Discrepancy Conditional Independence Test. In: Conference on Uncertainty in Artificial Intelligence.
Lee, S. and Honavar, V. (2017). A Kernel Independence Test for Relational Data. In: Conference on Uncertainty in Artificial Intelligence.
El-Manzalawy, Y., Buxton, O., and Honavar, V. (2017). Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering. In: IEEE Conference on Bioinformatics and Biomedicine.
El-Manzalawy, Y., Hsieh, T-Y., Shivkumar, M., Kim, D., and Honavar, V. (2017). Min-Redundancy and Max-Relevance Multi-view Feature Selection for Predicting Ovarian Cancer Survival using Multi-omics Data. In: The 7th Annual Translational Bioinformatics Conference.
El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2017). In silico prediction of linear B-cell epitopes on proteins. In: Y. Zhou, E. Faraggi, A. Kloczkowski and Y. Yang (Eds.), Prediction of Protein Secondary Structure, Methods in Molecular Biology, vol. 1484, DOI:10.1007/978-1-4939-6406-2_17.
Hager, G., Bryant, R., Horvitz, E., Mataric, M., and Honavar, V. (2017). Advances in Artificial Intelligence Require Progress Across all of Computer Science. A white paper prepared for the Computing Community Consortium Committee of the Computing Research Association.
Honavar, V., Yelick, K., Nahrstedt, K., Rushmeier, H., Rexford, J., Hill, Mark., Bradley, E., and Mynatt, E. (2017). Advanced Cyberinfrastructure for Science, Engineering, and Public Policy. A white paper prepared for the Computing Community Consortium committee of the Computing Research Association. arXiv preprint arXiv:1707.00599.
Walia, R., El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2017). Sequence-based Prediction of RNA-binding Residues in Proteins. Methods in Molecular Biology, vol. 1484, DOI:10.1007/978-1-4939-6406-2_15.
Xue, L., Rodrigues, J.P.L.M., Dobbs, D., Honavar, V., and Bonvin, A. (2017). Template-Based Protein-Protein Docking Improved Using Pairwise Interfacial Residue Restraints. Briefings in Bioinformatics doi: 10.1093/bib/bbw027
Bui, N., Le, T. and Honavar, V. (2016). Labeling Actors in Multi-view Social Networks by Integrating Information From Within and Across Multiple Views. In: Proceedings of the IEEE Conference on Big Data.
Bui, N., Yen, J., and Honavar, V. (2016). Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network. IEEE Transactions on Computational Social Systems. doi:10.1109/TCSS.2016.2591880.
Honavar, V., Hill, M., and Yelick, K. (2016). Accelerating Science: A Computing Research Agenda. A white paper prepared for the Computing Community Consortium committee of the Computing Research Association. arXiv preprint arXiv:1604.02006.
Lee, S. and Honavar, V. (2016). A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics In: Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI 2016), pp. 387-396.
Lee, S. and Honavar, V. (2016). On learning causal models from relational data. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16). pp.3262-3270.
El-Manzalawy, Y., Munoz, E., Lindner, S.E., and Honavar, V. (2016). PlasmoSEP: Predicting surface-exposed proteins on themalaria parasite using semisupervised self-training and expert-annotated data. Proteomics. doi: DOI 10.1002/pmic.201600249.
El-Manzalawy, Y., Abbas, M., Malluhi, Q., and Honavar, V. (2016). FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues. PLoS One 11(7): e0158445. doi:10.1371/journal.pone.0158445
Bui, N., Yen, J. and Honavar, V. (2015). Temporal Causality of Social Support in an Online Community for Cancer Survivors In: International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP15). Springer-Verlag Lecture Notes in Computer Science, Vol. 9021, pp. 13-23.
Lee, S., and Honavar, V. (2015). Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning In: Workshop on Advances in Causal Inference, Conference on Uncertainty in Artificial Intelligence, 2015.
Lin, H., Bui, N., and Honavar, V. (2015). Learning Classifiers from Remote RDF Data Stores Augmented with RDFS Subclass Hierarchies. In: 2nd International Workshop on High Performance Big Graph Data Management, Analysis,and Mining (BigGraph 2015), The IEEE International Conference on Big Data.
Sawyer, A., Kang, Y., Honavar, V., Griffin, P., and Prabhu, V. (2015). Stimulating new and innovative perspectives on old and persistent problems: A commentary on Wohlgemuth, et al. “Attempters, adherers, and non-adherers: Latent profile analysis of CPAP use with correlates. Sleep Medicine, Vol. 16. pp. 311-312.
Xue, L., Dobbs, D., Bonvin, A., and Honavar, V. (2015). Computational Prediction of Protein Interfaces: A Review of Data Driven Methods. FEBS Letters. Vol. 589, No. 23, pp. 3516-3526.
Bui, N. and Honavar, V. (2014). Labeling Actors in Social Networks Using a Heterogeneous Graph Kernel. In: International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP14). pp. 27-34.
El-Manzalawy. Y. and Honavar, V. (2014). Building Classifier Ensembles for B-Cell Epitope Prediction. In: De, R.K. and Tomar, N. (Ed). Immunoinformatics, Springer Protocols Methods in Molecular Biology, Vol. 1184. pp. 285-294.
Honavar, V. (2014). The Promise and Potential of Big Data: A Case for Discovery Informatics Review of Policy Research 31:4 10.1111/ropr.12080.
Tao, J., Slutzki, G., and Honavar, V. (2014). A Conceptual Framework for Secrecy-preserving Reasoning in Knowledge Bases. ACM Transactions on Computational Logic 16:1 DOI: http://dx.doi.org/10.1145/2637477.
Walia, RR., Xue, LC., Wilkins, K., El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2014). RNABindRPlus: A Predictor that Combines Machine Learning and Sequence Homology-Based Methods to Improve the Reliability of Predicted RNA-Binding Residues in Proteins, PloS one 9 (5), e97725
Xue, L., Jordan, R., El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2014) DockRank: Ranking Docked Conformations Using Partner-Speciﬁc Sequence Homology Based Protein Interface Prediction. Proteins: Structure, Function and Bioinformatics. Vol. 82, pp. 250-267. DOI: 10.1002/prot.24370.
Bareinboim, E., Lee, S., Honavar, V. and Pearl, J. (2013). Transportability from Multiple Environments with Limited Experiments. In: Advances in Neural Information Systems (NIPS) 2013. pp. 136-144.
Lee, S. and Honavar, V. (2013). Transportability of a Causal Effect from Multiple Environments. In: Proceedings of the 27th Conference on Artificial Intelligence (AAAI 2013).
Lee, S. and Honavar, V. (2013). Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability. In: Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013).
Lin, H. and Honavar, V. (2013). Learning Classifiers from Chains of Multiple Interlinked RDF Data Stores. In: IEEE Big Data Congress. Best Student Paper Award.
Lin, H., Lee, S., Bui, N. and Honavar, V. (2013). Learning Classifiers from Distributional Data. In: IEEE Big Data Congress.
Bui, N. and Honavar, V. (2013). On the Utility of Abstraction in Labeling Actors in Social Networks. In: The 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. In press.
Andorf, C., Honavar, V. and Sen, T. (2013). Predicting the Binding Patterns of Proteins: A Study Using Yeast Protein Interaction Networks. PLOS One 8(2): e56833, doi: 10.1371/journal.pone.0056833
Kumar, S, Nilsen, W., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., Riley, W. T, Shar, A., Spring, B., Spruijt-Metz, D., Hedeker, D, Honavar, V., Kravitz, R.L., R. Lefebvre, C., Mohr, D.C., Murphy, S.A., Quinn, C., Shusterman, V., Swendeman, D. (2013). Exploring Innovative Methods to Evaluate the Efficacy and Safety of Mobile Health. American Journal of Preventive Medicine, 45(2):228-236.
Oster, Z., Santhanam, G., Basu, S. and Honavar, V. (2013). Model Checking of Qualitative Sensitivity Preferences to Minimize Credential Disclosure. International Symposium on Formal Aspects of Component Software. Springer-Verlag Lecture Notes in Computer Science Vol. 7684, pp. 205-223, 2013.
Letao Qi, Harris T. Lin, Vasant Honavar: Clustering remote RDF data using SPARQL update queries. In: The 4th International Workshop on Graph Data Management: Techniques and Applications (GDM 2013), ICDE Workshops 2013: 236-242
El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2012). Predicting protective bacterial antigens using random forest classifiers.. ACM Conference on Bioinformatics and Computational Biology. pp. 426-433, 2012.
Jordan, R., El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2012). Predicting protein-protein interface residues using local surface structural similarity. BMC Bioinformatics 2012, 13:41 doi:10.1186/1471-2105-13-41. Highly Accessed.
Tao, J., Slutzki, G., and Honavar, V. (2012). PSpace Tableau Algorithms for Acyclic Modalized ALC. Journal of Automated Reasoning. Vol. 49. pp. 551-582
Towfic, F., Gupta, S., Honavar, V., and Subramaniam, S. (2012). B-Cell Ligand Processing Pathways Detected by Large-Scale Gene Expression Analysis. Genomics, Proteomics, and Bioinformatics. Vol. 10. pp. 142-152.
Towfic, F., Kohutyuk, O., Greenlee, MHW., and Honavar, V. (2012). Bionetworkbench: Database and Software for Storage, Query, and Interactive Analysis of Gene and Protein Networks. Bioinformatics and Biology Insights. Vol. 6. pp. 235-246.
Tu, K. and Honavar, V. (2012). Unambiguity Regularization for Unsupervised Learning of Probabilistic Grammars. In: Proceedings of EMNLP-CoNLL 2012 : Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. pp. 1324-1334.
Walia, R., Caragea, C., Lewis, B., Towfic, F., Terribilini, M., El-Manzalawy, Y., Dobbs, D., Honavar, V. (2012). Protein-RNA Interface Residue Prediction Using Machine Learning: An Assessment of the State of the Art. BMC Bioinformatics 13:89 doi:10.1186/1471-2105-13-89.
El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2011). Predicting MHC-II binding affinity using multiple instance regression. IEEE/ACM Transactions on Computational Biology and Bioinformatics. DOI: 10.1109/TCBB2010.94
Lewis, B.A., Walia, R.R., Terribilini, M., Ferguson, J., Zheng, C., Honavar, V., and Dobbs, D. (2011). PRIDB: A Protein-RNA Interface Database. Nucleic Acids Research. D277-282. DOI: 10.1093/nar/gkq1108.
Lin, H., Koul, N., and Honavar, V. (2011). Learning Relational Bayesian Classifiers from RDF Data. In: Proceedings of the International Semantic Web Conference (ISWC 2011). Springer-Verlag Lecture Notes in Computer Science Vol. 7031 pp. 389-404.
Muppirala, U., Honavar, V., and Dobbs, D. (2011). Predicting RNA-Protein Interactions Using Only Sequence Information. BMC Bioinformatics 2011, 12:489, doi:10.1186/1471-2105-12-489
Santhanam, G., Basu, S., and Honavar, V. (2011). Representing and Reasoning with Qualitative Preferences for Compositional Systems. Journal of Artificial Intelligence Research Vol 42, pp. 211-274.
Santhanam, G., Suvorov, Y., Basu, S., and Honavar, V. (2011). Verifying Intervention Policies for Countering Infection Propagation over Networks: A Model Checking Approach. In: Proceedings of the Twenty-Fifth Conference on Artificial Intelligence (AAAI-2011). pp. 1408-1414.
Santanam, G., Basu, S., and Honavar, V. (2011). Identifying Sustainable Designs Using Preferences Over Sustainability Attributes. In: AAAI Spring Symposium on Artificial Intelligence. pp. 91-97.
Silvescu, A. and Honavar, V. (2011). Abstraction Super-structuring Normal Forms: Towards a Theory of Structural Induction. In: The Proceedings of the Solomonoff 85th Memorial Conference. Springer-Verlag Lecture Notes in Artificial Intelligence. In press.
Tu, K. and Honavar, V. (2011). On the Utility of Curricula in Unsupervised Learning of Grammars. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011) pp. 1523-1528.
Tu, K., Ouyang, X., Han, D., Yu, Y., and Honavar, V. (2011). Exemplar-based Robust Coherent Biclustering. In: Proceedings of the SIAM Conference on Data Mining (SDM 2011). pp. 884-895.
Xue, L., Dobbs, D., and Honavar, V.. (2011). HomPPI: A Class of Sequence Homology Based Protein-Protein Interface Prediction Methods. BMC Bioinformatics 12:244 doi:10.1186/1471-2105-12-244
Xue, L., Jordan, R., El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2011). Sequence Based Partner-Specific Prediction of Protein- Protein Interfaces and its Application in Ranking Docked Models. In: ACM Conference on Bioinformatics and Computational Biology. ACM Press.
Yakhnenko, O., and Honavar, V. (2011). Multi-Instance Multi-Label Learning for Image Classification with Large Vocabularies. In: British Machine Vision Conference. In press.
Barnhill, A.E., Hecker, L.A., Kohutyuk, O., Buss, J.E., Honavar, V. and Greenlee, H.W. (2010) Characterization of the Retinal Proteome During Rod Photoreceptor Genesis. BMC Research Notes 3:25.
Caragea, C., Silvescu, A., Caragea, D. and Honavar, V. (2010). Abstraction-Augmented Markov Models. In: Proceedings of the IEEE Conference on Data Mining (ICDM 2010). IEEE Press. pp. 68-77.
Caragea, C. Silvescu, A., Caragea, D., and Honavar, V. (2010). Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models. BMC Bioinformatics. doi: 10.1186/1471-2105-11-S8-S6.
Caragea, C., Silvescu, A., Caragea, D., and Honavar, V. (2010). Semi-Supervised Sequence Classification Using Abstraction Augmented Markov Models. In: Proceedings of the ACM Conference on Bioinformatics and Computational Biology. pp. 257-264, doi: 10.1145/1854776.1854813. ACM Press.
El-Manzalawy, Y. and Honavar, V. (2010). Recent Advances in B-Cell Epitope Prediction Methods. Immunome Research Suppl. 2:S2.
Koul, N., Bui, N., and Honavar, V. (2010). Scalable, Updatable Predictive Models for Sequence Data. In Proceedings of the IEEE Intenational Conference on Bioinformatics and Biomedicine (BIBM 2010).
Koul, N. and Honavar, V. (2010). Learning in the Presence of Ontology Mapping Errors. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. pp. 291-296. ACM Press.
Pandit, S., and Honavar, V. (2010). Ontology-Guided Extraction of Complex Nested Relationships from Text. IEEE Conference on Tools With Artificial Intelligence (ICTAI 2010). pp. 173-178.
Sanghvi, B., Koul, N., and Honavar, V. (2010). Identifying and Eliminating Inconsistencies in Mappings across Hierarchical Ontologies. In: Springer-Verlag Lecture Notes in Computer Science Vol. 6427, pp. 999-1008. Berlin: Springer.
Santhanam, G., Basu, S., and Honavar, V. (2010). Efficient Dominance Testing for Unconditional Preferences. In: Proceedings of the Twelfth International Conference on the Principles of Knowledge Representation and Reasoning (KR 2010). pp. 590-592. AAAI Press.
Santhanam, G., Basu, S., and Honavar, V. (2010). Dominance Testing Via Model Checking. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI-10). pp. 357-362. AAAI Press.
Sun, H., Basu, S., Honavar, V., and Lutz, R. (2010). Automata-Based Verification of Security Requirements of Composite Web Services. In: Proceedings of the IEEE International Symposium on Software Reliability Engineering (ISSRE-2010). pp. 348-357, IEEE Press.
Tao, J., Slutzki, G., and Honavar, V. (2010). Secrecy-preserving Query Answering for Instance Checking in EL. In: Proceedings of the 4th International Conference on Web Reasoning and Rule Systems (RR 2010). Lecture Notes in Computer Science vol. 6333 pp. 195-203. Berlin: Springer.
Towfic, F., Caragea, C., Dobbs, D., and Honavar, V. (2010). Struct-NB: Predicting protein-RNA binding sites using structural features. International Journal of Data Mining and Bioinformatics, Vol 4. pp. 21-43.
Towfic, F., VanderPlas, S., Oliver, C.A., Couture, O., Tuggle, C.K., Greenlee, M.H.W., and Honavar, V. (2010). Detection of gene orthology from gene co-expression and protein interaction networks. BMC Bioinformatics, 11(Suppl 3):S7
Tuggle, C.K., Bearson, S.M.D, Huang, T.H., Couture, O., Wang, Y., Kuhar, D., Lunney, J.K., Honavar, V. (2010). Methods for transcriptomic analyses of the porcine host immune response: Application to Salmonella infection using microarrays. Veterinary Immunology and Immunopathology. Vol. 138. pp. 282-291.
Bao, J., Voutsadakis G., Slutzki, G. Honavar:, V. (2009). Package-Based Description Logics. In: Modular Ontologies: Concepts, Theories and Techniques for Knowledge Modularization. Lecture Notes in Computer Science Vol. 5445, pp. 349-371
Bromberg, F., Margaritis, D., and Honavar, V. (2009). Efficient Markov Network Structure Discovery from Independence Tests. Journal of Artificial Intelligence Research. Vol. 35. pp. 449-485.
Caragea, C., Sinapov, J., Dobbs, D., and Honavar, V. (2009). Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling. BMC Bioinformatics. doi:10.1186/1471-2105-10-S4-S4
Caragea, C., Caragea, D., and Honavar, V. (2009). Learning Link-Based Classifiers from Ontology-Extended Textual Data. In: Proceedings of the IEEE Conference on Tools with Artificial Intelligence.
Caragea, C., Caragea, D., and Honavar, V. (2009). Learning Link-Based Classifiers from Ontology-Extended Distributed Data. In: Proceedings of the 8th International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE). Springer-Verlag Lecture Notes in Computer Science Vol. 5871 pp 1139-1146, Berlin: Springer.
Couture, O., Callenberg, K., Koul, N., Pandit, S., Younes, J., Hu, Z-L., Dekkers, J., Reecy, J., Honavar, V., and Tuggle, C. (2009). ANEXdb: An Integrated Animal ANnotation and Microarray EXpression Database. Mammalian Genome. DOI 10.1007/s00335-009-9234-1
El-Manzalawi, Y. and Honavar, V. (2009). MICCLLR: Multiple-Instance Learning using Class Conditional Log Likelihood Ratio. In: Proceedings of the 12th International Conference on Discovery Science (DS 2009). Springer-Verlag Lecture Notes in Computer Science Vol. 5808, pp. 80-91, Berlin: Springer.
Koul, N., and Honavar, V. (2009). Design and Implementation of a Query Planner for Data Integration. In: Proceedings of the IEEE Conference on Tools with Artificial Intelligence.
Pham, H., Santhanam, G., McCalley, J., and Honavar, V. (2009). BenSOA: a Flexible Service-Oriented Architecture for Power System Asset Management. In Proceedings of the North American Power Symposium (NAPS).
Santhanam, G.R., Basu, S., and Honavar, V. (2009). Web Service Substitution Based on Preferences Over Non-functional Attributes. In: Proceedings of the IEEE International Conference on Services Computing (SCC 2009).
Silvescu, A., Caragea, C. and Honavar, V. (2009). Combining Super-structuring and Abstraction on Sequence Classification. IEEE Conference on Data Mining (ICDM 2009).
Towfic, F., Greenlee, H., and Honavar, V. (2009). Aligning Biomolecular Networks Using Modular Graph Kernels. In: Proceedings of the 9th Workshop on Algorithms in Bioinformatics (WABI 2009). Berlin: Springer-Verlag: LNBI Vol. 5724, pp. 345-361.
Towfic, F., Greenlee, H., and Honavar, V. (2009). Detecting Orthologous Genes Based on Protein-Protein Interaction Networks. In: Proceedings of the IEEE Conference on Bioinformatics and Biomedicine (BIBM 2009). IEEE Press.
Yakhnenko, O., and Honavar, V. (2009). Multi-Modal Hierarchical Dirichlet Process Model for Predicting Image Annotation and Image-Object Label Correspondence. In: Proceedings of the SIAM Conference on Data Mining, SIAM. pp. 281-294
Bao, J., Voutsadakis, G., Slutzki, G., and Honavar, V. (2008). On the Decidability of Role Mappings between Modular Ontologies. In: Proceedings of the 23nd Conference on Artificial Intelligence (AAAI-2008), Menlo Park, CA: AAAI Press, pp. 400-405
Caragea, C., Sinapov, J., Dobbs, D., and Honavar, V. (2008). Using Global Sequence Similarity to Enhance Macromolecular Sequence Labeling. IEEE Conference on Bioinformatics and Biomedicine, IEEE Press, pp. 104-111.
Caragea D., Cook, D., Wickham H. and Honavar, V. (2008). Visual Methods for Examining SVM Classifiers. Simeon J. Simoff, Michael H. Bohlen, Arturas Mazeika (Eds.): Visual Data Mining - Theory, Techniques and Tools for Visual Analytics. Springer-Verlag Lecture Notes in Computer Science Vol. 4404 pp.136-153
Dunn-Thomas, T., Dobbs, D.L., Sakaguchi, D. Young, M.J. Honavar, V. Greenlee, H. M. W. (2008). Proteomic Differentiation Between Murine Retinal and Brain Derived Progenitor Cells. Stem Cells and Development. 17:119-131.
El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2008). On Evaluating MHC-II Binding Peptide Prediction Methods. PLoS One, 3(9): e3268. doi:10.1371/journal.pone.0003268
El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2008). Predicting Flexible Length Linear B-cell Epitopes, 7th International Conference on Computational Systems Bioinformatics, Stanford, CA. Singapore: World Scientific.
El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2008). Predicting linear B-cell epitopes using string kernels. Journal of Molecular Recognition, DOI: 10.1002/jmr.893
El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2008). Predicting Protective Linear B-cell Epitopes using Evolutionary Information. IEEE Conference on Bioinformatics and Biomedicine, pp. 289-292, IEEE Press.
Hecker, L., Alcon, T., Honavar, V., and Greenlee, H. Analysis and Interpretation of Large-Scale Gene Expression Data Sets Using a Seed Network. Journal of Bioinformatics and Biology Insights. Vol. 2. pp. 91-102, 2008.
Hughes, LaRon, Bao, J., Honavar, V., and Reecy, J. (2008). Animal Trait Ontology (ATO): the importance and usefulness of a unified trait vocabulary for animal species. Journal of Animal Science, 86: 1485-1491.
Jo, H., Na, Y-C.,, Oh, B., Yang, J., and Honavar, V. (2008). Attribute Value Taxonomy Generation through Matrix Based Adaptive Genetic Algorithm. In: Proceedings of the IEEE Conference on Tools with Artificial Intelligence (ICTAI), IEEE Press, pp. 393-400
Koul, N., Caragea, C., Bahirwani, V., Caragea, D., and Honavar, V. (2008). Learning Classifiers from Large Databases Using Statistical Queries. In: Proceedings of the ACM/IEEE/WIC Conference on Web Intelligence, pp. 923-926.
Koul, N., Lathrop, J., Lutz, J., and Honavar, V. (2008). Complexes of Online Self-Assembly. IEEE International Conference on Electro-Information Technology. IEEE Press. pp. 448-452.
Lee. J-H., Hamilton, M., Gleeson, C., Caragea, C., Zaback, P., Sander, J., Lee, X., Wu, F., Terribilini, M., Honavar, V. and Dobbs, D. Striking Similarities in Diverse Telomerase Proteins Revealed by Combining Structure Prediction and Machine Learning Approaches.. In Proceedings of the Pacific Symposium on Biocomputing (PSB 2008). Vol. 13. pp. 501-512, 2008.
Pathak, J., Basu, S., and Honavar, V. (2008). Composing Web Services through Automatic Reformulation of Service Specifications. Proceedings of the IEEE International Conference on Services Computing, IEEE, pp. 361-369.
Pathak, J., Basu, S., Lutz, R., and Honavar, V. (2008). MoSCoE: An Approach for Composing Web Services through Iterative Reformulation of Functional Specifications. International Journal of Artificial Intelligence Tools, Vol. 17. No. 1. pp. 109-138, 2008.
Peto M., Kloczkowski A., Honavar V., Jernigan R.L. (2008). Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable. BMC Bioinformatics, 9:487-.
Santhanam, G., Basu, S., and Honavar, V. (2008). TCP-Compose* - A TCP-net based Algorithm for Efficient Composition of Web Services Based on Qualitative Preferences. Proceedings of the 6th International Conference on Service Oriented Computing, Springer-Verlag Lecture Notes in Computer Science, Vol. 5254. pp. 453-467
Tu, K., and Honavar, V. (2008). Unsupervised Learning of Probabilistic Context-Free Grammar using Iterative Biclustering. . In: International Colloquium on Grammatical Inference (ICGI-2008). Springer-Verlag Lecture Notes in Computer Science vol. 5278 pp. 224-237.
Voutsadakis, G., Bao, J., Slutzki, G., and Honavar, V. (2008). F-ALCI: A Fully Contextualized, Federated Logic for the Semantic Web. Proceedings of the ACM/IEEE/WIC Conference on Web Intelligence, Sydney, Australia.
Yakhnenko, O. and Honavar, V. (2008). Annotating Images and Image Objects using a Hierarchical Dirichlet Process Model. 9th International Workshop on Multimedia Data Mining (SIGKDD MDM 2008), Las Vegas, ACM.
Yan, C., Wu, F., Dobbs, D., Jernigan, R., and Honavar, V. (2008). Characterization of Protein-Protein Interfaces. The Protein journal. doi:10.1007/s10930-007-9108-x
Andorf, C., Dobbs, D. and Honavar, V. (2007). Exploring Inconsistencies in Genome Wide Protein Function Annotations: A Machine Learning Approach. BMC Bioinformatics 8:284 doi:10.1186/1471-2105-8-284
Bao, J., Slutzki, G., and Honavar, V. (2007). A Semantic Importing Approach to Knowledge Reuse from Multiple Ontologies.. In: Proceedings of the 22nd Conference on Artificial Intelligence (AAAI-2007). Vancouver, Canada. Semantic Importing Approach to Knowledge Reuse from Multiple Ontologies. pp. 1304-1309. AAAI Press.
Bao, J., Slutzki, G., and Honavar, V. (2007). Privacy-Preserving Reasoning on the Semantic Web. IEEE/WIC/ACM Conference on Web Intelligence. IEEE. pp. 791-797
Caragea, C., Sinapov, J., Dobbs, D., and Honavar, V. (2007). Assessing the Performance of Macromolecular Sequence Classifiers, In: Proceedings of the IEEE Conference on Bioinformatics and Bioengineering (BIBE 2007). pp. 320-326, 2007.
Caragea, C., Sinapov, J., Silvescu, A., Dobbs, D. And Honavar, V. (2007). Glycosylation Site Prediction Using Ensembles of Support Vector Machine Classifiers. BMC Bioinformatics. doi:10.1186/1471-2105-8-438.
Helmer, G., Wong, J., Slagell, M., Honavar, V., Miller, L., Wang, Y., Wang, X., and Stakhanova, N. (2007). Software Fault Tree and Colored Petri Net Based Specification, Design, and Implementation of Agent-Based Intrusion Detection Systems. International Journal of Information and Computer Security. Vol. 1. No. 1/2. pp. 109-142, 2007
J. McCalley, V. Honavar, S. Ryan, W. Meeker, D. Qiao, R. Roberts, Y. Li, J. Pathak, M. Ye, Y. Hong (2007). Integrated Decision Algorithms for Auto-steered Electric Transmission System Asset Management. 7th Intl. Conference on Computational Science, Berlin: Springer-Verlag. Vol. 4487. pp. 1066-1073.
Pathak, J., Basu, S., and Honavar, V. (2007). On Context-Specific Substitutability of Web Services. In: Proceedings of the IEEE International Conference on Web Services. pp. 192-199. IEEE Press.
Pathak, J., Li, Y., Honavar, V., McCalley , J. (2007). A Service-Oriented Architecture for Electric Power Transmission System Asset Management. Second International Workshop on Engineering Service-Oriented Applications: Design and Composition, Lecture Notes in Computer Science, Berlin: Springer-Verlag, 2007.
Terribilini, M., Sander, J.D., Lee, J-H., Zaback, P., Jernigan, R.L., Honavar, V. and Dobbs, D. (2007). RNABindR: A Server for Analyzing and Predicting RNA Binding Sites in Proteins. Nucleic Acids Research. doi:10.1093/nar/gkm294
Wu, F., Towfic, F., Dobbs, D. and Honavar, V. (2007). Analysis of Protein Protein Dimeric Interfaces. IEEE International Conference on Bioinformatics and Biomedicine.
Bao, J., Caragea, D., and Honavar, V. (2006). On the Semantics of Linking and Importing in Modular Ontologies.In: Proceedings of the International Semantic Web Conference (ISWC 2006), Lecture Notes in Computer Science, Berlin: Springer. Lecture Notes in Computer Science Vol. 4273, pp. 72-86.
Bao, J., Caragea, D., and Honavar, V. (2006). A Tableau Based Federated Reasoning Algorithm for Modular Ontologies. In: Proceedings of the ACM/IEEE/WIC Conference on Web Intelligence. IEEE Press. pp. 404-410.
Bao, J., Hu, Z., Caragea, D., Reecy, J., and Honavar, V. A Tool for Collaborative Construction of Large Biological Ontologies. Fourth International Workshop on Biological Data Management (BIDM 2006), Krakov, Poland, IEEE Press. pp. 191-195.
Bao, J., Caragea, D., and Honavar, V. A Distributed Tableau Algorithm for Package-based Description Logics. Proceedings of the Second International Workshop on Context Representation and Reasoning (CRR 2006), Riva del Garda, Italy, CEUR. 2006.
Bao, J., Caragea, D., and Honavar, V. Modular Ontologies - A Formal Investigation of Semantics and Expressivity. In Proceedings of the First Asian Semantic Web Conference, Beijing, China, Springer-Verlag. Vol. Vol. 4185, pp. 616-631, 2006. Best Paper Award
Bao, J., Caragea, D., and Honavar, V. Towards Collaborative Environments for Ontology Construction and Sharing. Proceedings of the International Symposium on Collaborative Technologies and Systems., Las Vegas, 2006.
Bromberg, F., Margaritis, D., and Honavar, V. Efficient Markov Network Structure Discovery from Independence Tests. SIAM Conference on Data Mining (SDM 06), SIAM Press, 2006.
Caragea, D., Zhang, J., Pathak, J., and Honavar, V. (2006) Learning Classifiers from Distributed, Ontology-Extended Data Sources. Proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2006), Krakov, Poland, IEEE Press, pp. 363-373.
Kang, D-K., Silvescu, A. and Honavar, V. (2006) RNBL-MN: A Recursive Naive Bayes Learner for Sequence Classification. Proceedings of the Tenth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). Lecture Notes in Computer Science., Berlin: Springer-Verlag. pp. 45-54, 2006.
Lee, K., Joo, J., Yang, J., and Honavar, V. (2006). Experimental Comparison of Feature Subset Selection Using GA and ACO Algorithm. In: Proceedings of Second International Conference on Advanced Data Mining and Applications, Berlin: Springer-Verlag. Lecture Notes in Computer Science, Vol. 4093, pp. 465-472.
Pathak, J., Basu, S., Lutz, R., and Honavar, V. (2006). Selecting and Composiing Web Services through Iterative Reformulation of Functional Specifications. Proceedings of the IEEE International Conference on Tools With Artificial Intelligence (ICTAI 2006), Washington, DC, IEEE Press. Best Paper Award. pp. 445-454.
Pathak, J., Basu, S., and Honavar, V. (2006). Modeling Web Services by Iterative Reformulation of Functional and Non-Functional Requirements. Proceedings of the International Conference on Service Oriented Computing. Lecture Notes in Computer Science, Berlin: Springer, Vol. 4294, pp. 314-326.
Pathak, J., Yuan, L., Honavar, V., and McCalley, J. (2006). A Service-Oriented Architecture for Electric Power Transmission System Asset Management, In: Proceedings of the Second International Workshop on Engineering Service-Oriented Applications: Design and Composition (WESOA-2006), Lecture Notes in Computer Science, Berlin: Springer-Verlag.
Pathak, J., Basu, S., Lutz, R., and Honavar, V. (2006). Parallel Web Service Composition in MoSCoE: A Choreography Based Approach. Proceedings of the IEEE European Conference on Web Services (ECOWS 2006), Zurich, Switzerland, IEEE. In press.
Pathak, J. Basu, S., Lutz, R. and Honavar, V. MoSCoE: A Framework for Modeling Web Service Composition and Execution. IEEE Conference on Data Engineering Ph.D. Workshop, Atlanta, GA, 2006.
Pathak, J., Basu, S., and Honavar, V. Modeling Web Service Composition Using Symbolic Transition Systems. AAAI '06 Workshop on AI-Driven Technologies for Services-Oriented Computing (AI-SOC), Boston, MA, AAAI Press, 2006.
Pathak, J, Yong, J. Honavar, V., McCalley, J. Condition Data Aggregation for Failure Mode Estimation of Power Transformers. Hawaii International Conference on Systems Sciences, IEEE Computer Society. pp. 241a, 2006.
Silvescu, A. and Honavar, V. Independence, Decomposability and functions which take values into an Abelian Group. Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics, http://anytime.cs.umass.edu/aimath06/proceedings.html, 2006.
Terribilini, M., Lee. J-H., Yan, C., Carpenter, S., Jernigan, R., Honavar, V. and Dobbs, D. Identifying interaction sites in recalcitrant proteins: predicted protein and rna binding sites in HIV-1 and EIAV agree with experimental data. Pacific Symposium on Biocomputing, Hawaii, World Scientific. Vol. 11. pp. 415-426, 2006.
Terribilini, M., Lee, J.-H., Yan, C., Jernigan, R. L., Honavar, V. and Dobbs, D. (2006). Predicting RNA-binding Sites from Amino Acid Sequence. In: RNA Journal.. Vol. 12. No. 1450. pp. 1462.
Vasile, F., Silvescu, A., Kang, D-K., and Honavar, V. TRIPPER: An Attribute Value Taxonomy Guided Rule Learner. Proceedings of the Tenth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Berlin: Springer-Verlag. pp. 55-59, 2006.
Wu, F., Olson, B., Dobbs, D., and Honavar, V. (2006). Using Kernel Methods to Predict Protein-Protein Interaction Sites from Sequence. IEEE Joint Conference on Neural Networks, Vancouver, Canada, IEEE Press.
Yan, C., Terribilini, M., , Wu, F., Jernigan, R.L., Dobbs, D. and Honavar, V. (2006) Identifying amino acid residues involved in protein-DNA interactions from sequence. BMC Bioinformatics, 2006.
Zhang, J., Kang, D-K., Silvescu, A. and Honavar, V. (2006). Learning Compact and Accurate Naive Bayes Classifiers from Attribute Value Taxonomies and Data. Knowledge and Information Systems. Vol. 9. No. 2. pp. 157-179.
Wang, Y., Behera, S., Wong, J., Helmer, G., Honavar, V., Miller, L., and Lutz, R. (2006) Towards Automatic Generation of Mobile Agents for Distributed Intrusion Detection Systems. Journal of Systems and Software, Vol. 79, pp. 1-14, 2006.
Caragea, D., Zhang, J., Bao, J., Pathak, J., and Honavar, V. Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous Information Sources (Invited paper). Proceedings of the 16th International Conference on Algorithmic Learning Theory. Lecture Notes in Computer Science, Singapore, Berlin: Springer-Verlag. Vol. 3734. pp. 13-44, 2005.
Caragea, D., Bao, J., Pathak, J., Andorf, C,., Dobbs, D., and Honavar, V. Information Integration from Semantically Heterogeneous Biological Data Sources. Proceedings of the Sixteenth International Workshop on Databases and Expert Systems Applications (DEXA 05), Copenhagen, IEEE Computer Society. pp. 580-584, 2005.
Caragea, D., Silvescu, A., Pathak, J., Bao, J., Andorf, C., Dobbs, D., and Honavar, V. Information Integration and Knowledge Acquisition from Semantically Heterogeneous Biological Data Sources. Data Integration in Life Sciences (DILS 2005) Springer-Verlag Lecture Notes in Computer Science, San Diego, Berlin: Springer-Verlag. Vol. 3615. pp. 175-190, 2005.
Zhang, J., Caragea, D. and Honavar, V. Learning Ontology-Aware Classifiers. Proceedings of the 8th International Conference on Discovery Science. Springer-Verlag Lecture Notes in Computer Science, Singapore, Berlin: Springer-Verlag. Vol. 3735. pp. 308-321, 2005.
Pathak, J,, Koul, N., Caragea, D., and Honavar, V. A Framework for Semantic Web Services Discovery. Proceedings of the 7th ACM International Workshop on Web Information and Data Management (WIDM 2005)., ACM Press. pp. 45-50, 2005.
Yakhnenko, O., Silvescu, A., and Honavar, V. Discriminatively Trained Markov Model for Sequence Classification. IEEE Conference on Data Mining (ICDM 2005), Houston, Texas, IEEE Press, 2005.
Kang, D-K., Fuller, D., and Honavar, V. Learning Misuse and Anomaly Detectors from System Call Frequency Vector Representation. IEEE International Conference on Intelligence and Security Informatics. Springer-Verlag Lecture Notes in Computer Science, Springer-Verlag. Vol. 3495. pp. 511-516, 2005.
Kang, D-K., Zhang, J., Silvescu, A., and Honavar, V. Multinomial Event Model Based Abstraction for Sequence and Text Classification. Proceedings of the Symposium on Abstraction, Reformulation, and Approximation (SARA 2005), Edinburgh, UK, Berlin: Springer-Verlag. Vol. 3607. pp. 134-148, 2005.
Kang, D-K., Fuller, D., and Honavar, V. Learning Classifiers for Misuse and Anomaly Detection Using a Bag of System Calls Representation. Proceedings of the 6th IEEE Systems, Man, and Cybernetics Workshop (IAW 05), West Point, NY, IEEE. pp. 118-125, 2005.
Vasile, F., Silvescu, A., Kang, D-K., and Honavar, V. TRIPPER: Rule Learning Using Attribute Value Taxonomies. AAAI Workshop on Human-Comprehensible Machine Learning, Pittsburgh, PA, 2005.
Wu. F., Zhang, J., and Honavar, V. Learning Classifiers Using Hierarchically Structured Class Taxonomies. Proceedings of the Symposium on Abstraction, Reformulation, and Approximation (SARA 2005), Edinburgh, Berlin, Springer-Verlag. Vol. 3607. pp. 313-320, 2005.
Andorf, C., Silvescu, A., Dobbs, D. and Honavar, V. Learning Classifiers for Assigning Protein Sequences to Gene Ontology Functional Families. Fifth International Conference on Knowledge Based Computer Systems (KBCS 2004), India, New Delhi, India: Allied Publishers. pp. 256-255, 2004.
Bao, J. and Honavar, V. Collaborative Ontology Building With Wiki@nt. Third International Workshop on Evaluation of Ontology Building Tools, Hiroshima, 2004.
Bao, J., Cao, Y., Tavanapong, W., and Honavar, V. Integration of Domain-Specific and Domain-Independent Ontologies for Colonoscopy Video Database Annotation. International Conference on Information and Knowledge Engineeringl (IKE 04), Las Vegas, Nevada, USA, CSREA Press. pp. 82-88, 2004.
Caragea, D., Pathak, J. and Honavar, V. Learning Classifiers from Semantically Heterogeneous Data. International Conference on Ontologies, Databases, and Applications of Semantics (ODBASE 2004). Springer-Verlag Lecture Notes in Computer Science, Cyprus, Greece, Springer-Verlag. Vol. 3291. pp. 963-980, 2004.
Caragea, D., Silvescu, A., and Honavar, V. (2004) A Framework for Learning from Distributed Data Using Sufficient Statistics and its Application to Learning Decision Trees. International Journal of Hybrid Intelligent Systems. Vol. 1. No. 2. pp. 80-89, 2004.
Cook, D., Caragea, D., and Honavar, V. Visualization in Classification Problems. Proceedings in Computational Statistics (COMPSTAT 2004), Springer-Verlag. pp. 799-806, 2004.
Kang, D-K., Silvescu, A., Zhang, J. and Honavar, V. Generation of Attribute Value Taxonomies from Data for Accurate and Compact Classifier Construction. IEEE International Conference on Data Mining, IEEE Press. pp. 130-137, 2004.
Lonosky, P., Zhang, X., Honavar, V., Dobbs, D., Fu, A., and Rodermel, S. (2004) A Proteomic Analysis of Chloroplast Biogenesis in Maize. Plant Physiology. Vol. 134. pp. 560-574, 2004.
Pathak, J., Caragea, D., and Honavar, V. Ontology-Extended Component-Based Workflows: A Framework for Constructing Complex Workflows from Semantically Heterogeneous Software Components. VLDB-04 Workshop on Semantic Web and Databases. Springer-Verlag Lecture Notes in Computer Science., Toronto, Springer-Verlag. Vol. 3372. pp. 41-56, 2004.
R. Polikar, L. Udpa, S. Udpa, and V. Honavar (2004). An Incremental Learning Algorithm with Confidence Estimation for Automated Identification of NDE Signals. IEEE Transactions of Ultrasonics, Ferroelectrics, and Frequency Control. Vol. 51. pp. 990-1001, 2004.
Sen, T.Z., Kloczkowski, A., Jernigan, R.L., Yan, C., Honavar, V., Ho, K-M., Wang, C-Z., Ihm, Y., Cao, H., Gu, X., and Dobbs, D. Predicting Binding Sites of Protease-Inhibitor Complexes by Combining Multiple Methods. BMC Bioinformatics. Vol. 5. pp. 205, 2004.
Yan, C., Dobbs, D., and Honavar, V. A Two-Stage Classifier for Identification of Protein-Protein Interface Residues. Proceedings of the Conference on Intelligent Systems in Molecular Biology (ISMB 2004). 2004.
Yan, C., Dobbs, D., and Honavar, V. A Two-Stage Classifier for Identification of Protein-Protein Interface Residues. Bioinformatics. Vol. 20. pp. i371-378, 2004.
Yan, C., Dobbs, D., and Honavar, V. Identifying Protein-Protein Interaction Sites from Surface Residues A Support Vector Machine Approach. Neural Computing Applications. Vol. 13. pp. 123-129, 2004.
Zhang, Z.; McCalley, J.D.; Vishwanathan, V.; Honavar, V. Multiagent system solutions for distributed computing, communications, and data integration needs in the power industry. Proceedings of the General Meeting of the IEEE Power Engineering Society, IEEE Press. pp. 45-49, 2004.
Zhang, J. and Honavar, V. Learning Compact and Accurate Classifiers from Attribute Value Taxonomies and Partially Specified Data. IEEE International Conference on Data Mining, IEEE Press. pp. 289-298, 2004.
Atramentov, A., Leiva, H., and Honavar, V. (2003). A Multi-Relational Decision Tree Learning Algorithm - Implementation and Experiments.. In: Proceedings of the Thirteenth International Conference on Inductive Logic Programming. Berlin: Springer-Verlag.
Caragea, D., Silvescu, A., and Honavar, V. (2003). Decision Tree Induction from Distributed, Heterogeneous, Autonomous Data Sources. In: Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA 03).
Caragea, D., Reinoso-Castillo, J., Silvescu, A. (2003). Statistics Gathering for Information Integration on the Web. In: Proceedings of the IJCAI-03 Workshop on Information Integration on the Web..
Helmer, G., Wong, J., Honavar, V., and Miller, L. (2003). Lightweight Agents for Intrusion Detection. Journal of Systems and Software. Vol. 67. pp. 109-122.
Reinoso-Castillo, J., Silvescu, A., Caragea, D., Pathak, J. and Honavar, V. (2003). Information Extraction and Integration from Heterogeneous, Distributed, Autonomous Information Sources: A Federated, Query-Centric Approach.. IEEE International Conference on Information Integration and Reuse.
Wang, X., Schroeder, D., Dobbs, D., and Honavar, V. (2003). Automated Data-Driven Discovery of Motif-Based Protein Function Classifiers. Information Sciences. Vol. 155. pp. 1-18.
Yan, C., Dobbs, D. (2003). Identification of Surface Residues Involeved in Protein-Protein Interaction -- A Support Vector Machine ApproachIn: Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA-03). Tulsa, Oklahoma. 2003.
Zhang, J. and Honavar, V. (2003). Learning Decision Tree Classifiers from Attribute Value Taxonomies and Partially Specified Data. In: Proceedings of the International Conference on Machine Learning (ICML-03). Washington, DC.
Andorf, C., Dobbs, D., and Honavar, V. (2002). Discovering Protein Function Classification Rules from Reduced Alphabet Representations of Protein Sequences. In: Proceedings of the Conference on Computational Biology and Genome Informatics. Durham, North Carolina.
Wang, X., Schroeder, D., Dobbs, D., and Honavar, V. (2002). Data-Driven Discovery of Protein Function Classifiers: Decision Trees Based on MEME Motifs Outperform Those Based on PROSITE Patterns and Profiles on Peptidase Families. In: Proceedings of the Conference on Computational Biology and Genome Informatics. Durham, North Carolina.
Helmer, G., Wong, J., Slagell, M., Honavar, V., Miller, L., and Lutz, R. (2002) A Software Fault Tree Approach to Requirements Specification of an Intrusion Detection System. Requirements Engineering. Vol 7 (4) (2002) pp. 207-220.
Helmer, G., Wong, J., Honavar, V., and Miller, L. (2002). Automated Discovery of Concise Predictive Rules for Intrusion Detection. Journal of Systems and Software.60 (3) (2002) pp. 165-175
Zhang, J., Silvescu, A., and Honavar, V. (2002). Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction. In: Proceedings of Symposium on Abstraction, Reformulation, and Approximation. Berlin: Springer-Verlag.
Caragea, D., Cook, D., and Honavar, V. (2001). Gaining Insights into Support Vector Machine Classifiers Using Projection-Based Tour Methods. In: Proceedings of the Conference on Knowledge Discovery and Data Mining..
Helmer, G., Wong, J., Slagell, M., Honavar, V., Miller, L. and Lutz, R. (2001). A Software Fault Tree Approach to Requirements Analysis of an Intrusion Detection System. In: Proceedings of the Symposium on Requirements Engineering for Information Security, Indianapolis, IN, USA.
Polikar, R., Shinar, R., Honavar, V., Udpa, L., and Porter, M. (2001). Detection and Identification of Odorants Using an Electronic Nose. In: Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing.
Polikar, R., Udpa, L., Udpa, S., and Honavar, V. (2001). Learn++: An Incremental Learning Algorithm for Multi-Layer Perceptron Networks. IEEE Transactions on Systems, Man, and Cybernetics. Vol. 31, No. 4. pp. 497-508.
Mikler, A., Honavar, V. and Wong, J. (2001). Autonomous Agents for Coordinated Distributed Parameterized Heuristic Routing in Large Communication Networks. Journal of Systems and Software. Vol. 56. pp. 231-246.
Parekh, R. and Honavar, V. (2001). DFA Learning from Simple Examples. Machine Learning. Vol. 44. pp. 9-35.
Silvescu, A., and Honavar, V. (2001). Temporal Boolean Network Models of Genetic Networks and Their Inference from Gene Expression Time Series. Complex Systems.. Vol. 13. No. 1. pp. 54-.
Silvescu, A. and Honavar, V. (2001). Temporal Boolean Network Models of Genetic Networks and Their Inference from Gene Expression Time Series. In: Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems & Technology.
Viswanathan, V., McCalley, J., and Honavar, V. (2001). A Multi-agent System Infrastructure and Negotiation Framework for Electric Power Systems. In: Proceedings of the IEEE Power Technology Conference, Porto, Portugal, 2001.
Wang, D., Wang, X., Honavar, V., and Dobbs, D. (2001). Data-Driven Generation of Decision Trees for Motif-Based Assignment of Protein Sequences to Functional Families. In: Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems & Technology.
Wong, J., Helmer, G., Naganathan, V. Polavarapu, S., Honavar, V., and Miller, L. (2001) SMART Mobile Agent Facility. Journal of Systems and Software. Vol. 56. pp. 9-22.
Balakrishnan, K., Bousquet, O. and Honavar, V. (2000). Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots, Adaptive Behavior. Vol. 7. no. 2. pp. 173-216.
Caragea, D., Silvescu, A., and Honavar, V. (2000). Agents That Learn from Distributed Dynamic Data Sources. In: Proceedings of the ECML 2000/Agents 2000 Workshop on Learning Agents. Barcelona, Spain.
Caragea, D., Silvescu, A., and Honavar, V. (2000). Towards a Theoretical Framework for Analysis and Synthesis of Distributed and Incremental Learning Agents. In: Proceedings of the Workshop on Distributed and Parallel Knowledge Discovery. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston, U.S.A.
Chung, M. and Honavar, V. A Negotiation Model for Electronic Commerce, In: Proceedings of the IEEE Symposium on Multimedia Software Engineering. 2000.
Pai, P., Miller, L., Nilakanta, S., Honavar, V., and Wong, J. (2000). Supporting Organizational Knowledge Management with Agents. In: Proceedings of the Eleventh International Conference of the Information Resources Management Association, Anchorage, Alaska.
Parekh, R. and Honavar, V. (2000). On the Relationships between Models of Learning in Helpful Environments. In: Proceedings of the Fifth International Conference on Grammatical Inference. Lisbon, Portugal.
Parekh, R., Yang, J., and Honavar, V. (2000). Constructive Neural Network Learning Algorithms for Multi-Category Pattern Classification. IEEE Transactions on Neural Networks. Vol. 11. No. 2. pp. 436-451.
Polikar, R., Udpa, L., Udpa, S., and Honavar, V. (2000). Learn++: An Incremental Learning Algorithm for Multilayer Perceptron Networks. In: Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2000. Istanbul, Turkey.
Yang, J., Parekh, R. & Honavar, V. (2000). Comparison of Performance of Variants of Single-Layer Perceptron Algorithms on Non-Separable Data. Neural, Parallel, and Scientific Computation. Vol. 8. pp. 415-438.
Chen, C-H. & Honavar, V. (1999). A Neural Architecture for Syntax Analysis. IEEE Transactions on Neural Networks. Vol. 10 pp. 94-114.
Janakiraman, J. and Honavar, V. (1999). Adaptive Learning Rate for Speeding up Gradient Descent Learning. Microcomputer Applications. Vol. 18. pp. 89-95.
Yang, J. and Honavar, V. (1999). DistAl: An Inter-Pattern Distance Based Constructive Neural Network Learning Algorithm.. Intelligent Data Analysis. Vol. 3. pp. 55-73.
Balakrishnan, K. and Honavar, V. (1998). Intelligent Diagnosis Systems. Journal of Intelligent Systems.. Vol. 8. No.3/4. pp. 239-290.
Bhatt, R., Balakrishnan, K., and Honavar, V. (1999). A Constructive Neural Network Algorithm for Place Learning. In: Proceedings of the International Joint Conference on Neural Networks. Washington, D.C.
Dandu, R., L. Miller, S. Nilakanta, and V. Honavar. (1999). Populating a data warehouse with mobile agents. In: Proceedings of the The Tenth International Conference of the Information Resources Management Association. Hershey, PA.
Helmer, G., Wong, J., Honavar, V., and Miller, L. (1999). Data-Driven Induction of Compact Predictive Rules for Intrusion Detection from System Log Data. In: Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO 99). Orlando, Florida.
Parekh, R. and Honavar, V. (1999). Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples. In: Proceedings of the International Conference on Machine Learning. Bled, Slovenia.
Yang, J., Parekh, R., Honavar, V., and Dobbs, D. (1999). Data-Driven Theory Refinement Using KBDistAl . In: Proceedings of the Conference on Intelligent Data Analysis. Amsterdam, Holland.
Yang, J., Parekh, R., Honavar, V., and Dobbs, D. (1999). Data-Driven Theory Refinement Algorithms for Bioinformatics. In: Proceedings of the International Joint Conference on Neural Networks. Washington, D.C.
Balakrishnan, K., Bhatt, R., and Honavar, V. (1998). A Computational Model of Rodent Spatial Learning and Some Behavioral Experiments. In: Proceedings of the Twentieth Annual Meeting of the Cognitive Science Society. Madison, WI.
Balakrishnan, K., Bhatt, R., and Honavar, V. (1998). Spatial Learning and Localization in Animals: A Computational Model and Behavioral Experiments. In: Proceedings of the Second European Conference on Cognitive Modelling. pp. 112-11 9. Nottingham University Press.
Bousquet, O., Balakrishnan, K. and Honavar, V. (1998). Is the Hippocampus a Kalman Filter?. In: Proceedings of the Pacific Symposium on Biocomputing. Singapore: World Scientific. pp. 655-666.
Helmer, G., Wong, J., Honavar, V. and Miller, L. (1998). Intelligent Agents for Intrusion Detection. In: Proceedings of the IEEE Information Technology Conference. Syracuse, NY.
Honavar, V., Miller, L. and Wong, J. (1998). Distributed Knowledge Networks. In: Proceedings of the IEEE Information Technology Conference. Syracuse, NY.
Leavens, G., Baker, A., Honavar, V., Lavalle, S., and Prabhu, G. (1998). Programming is Writing: Why Student Programs Must be Carefully Read.. Vol. 32. pp. 284-295.
Mikler, A., Wong, J., and Honavar, V. (1998). An Object-Oriented Approach to Modelling and Simulation of Routing in Large Communication Networks. Journal of Systems and Software Vol. 40, pp. 151-164.
Miller, L., Honavar, V. and Wong, J. (1998). Object-Oriented Data Warehouse for Information Fusion from Heterogeneous Data and Knowledge Sources. In: Proceedings of the IEEE Information Technology Conference. Syracuse, NY.
Parekh, R. and Honavar, V. (1998). Constructive theory refinement in knowledge based neural networks. In: Proceedings of the International Joint Conference on Neural Networks. Anchorage, Alaska. pp. 2318-2323.
Parekh, R., Nichitiu, C., and Honavar, V. (1998). A Polynomial Time Incremental Algorithm for Learning DFA. In: Proceedings of the Fourth International Colloquium on Grammatical Inference (ICGI'98), Ames, IA. Lecture Notes in Computer Science vol. 1433 pp. 37-49. Berlin: Springer-Verlag.
Spartz, R. and Honavar, V. (1998). An Empirical Analysis of the Expected Source Values Rule. Microcomputer Applications. Vol. 17, pp. 29-34.
Yang, J., Parekh, R., and Honavar, V. (1998). Distal: An Inter-pattern distance-based constructive learning algorithm. In: Proceedings of the International Joint Conference on Neural Networks. Anchorage, Alaska. pp. 2208-2213.
Yang, J., Pai, P., Honavar, V., and Miller, L. (1998). Mobile Intelligent Agents for Document Classification and Retrieval: A Machine Learning Approach. In: Proceedings of the European Symposium on Cybernetics and Systems Research.
Yang, J., Honavar, V., Miller, L. and Wong, J. (1998). Intelligent Mobile Agents for Information Retrieval and Knowledge Discovery from Distributed Data and Knowledge Sources. In: Proceedings of the IEEE Information Technology Conference. Syracuse, NY.
Yang, J., Havaldar, R., Honavar, V., Miller, L. and Wong, J. (1998). Coordination and Control of Distributed Knowledge Networks Using the Contract Net Protocol. In: Proceedings of the IEEE Information Technology Conference. Syracuse, NY.
Yang, J. and Honavar, V. (1998). Experiments with the Cascade-Correlation Algorithm. Microcomputer Applications. Vol. 17 pp. 40-46.
Yang, J. and Honavar, V. (1998). Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems (Special Issue on Feature Transformation and Subset Selection). vol. 13. pp. 44-49.
Balakrishnan, K. and Honavar, V. (1997). Spatial Learning for Robot Localization. In: Proceedings of International Conference on Genetic Programming. Stanford, CA. pp. 389-397.
Mikler, A., Wong, J. and Honavar, V. (1997). Quo Vadis - A Framework for Intelligent Routing in Large Communication Networks.
Miller, L., Honavar, V. and Barta, T.A. (1997). Warehousing Structured and Unstructured Data for Data Mining. In: Proceedings of the American Society for Information Science Annual Meeting (ASIS 97). Washington, D.C.
Parekh, R.G., Yang, J., and Honavar, V. (1997). MUPStart - A Constructive Neural Network Learning Algorithm for Multi-Category Pattern Classification. In: Proceedings of IEEE International Conference on Neural Networks (ICNN'97). Houston, TX. pp. 1924-1929.
Parekh, R.G., Yang, J., and Honavar, V. (1997). Pruning Strategies for Constructive Neural Network Learning Algorithms. In: Proceedings of IEEE International Conference on Neural Networks (ICNN'97). Houston, TX. pp. 1960-1965. June 9-12, 1997.
Parekh, R.G. and Honavar, V. (1997) Learning DFA from Simple Examples. In: Proceedings of the International Workshop on Algorithmic Learning Theory. (ALT 97). Sendai, Japan. Lecture notes in Computer Science. Vol. 1316 pp. 116-131.
Yang, J. and Honavar, V. (1997). Feature Subset Selection Using a Genetic Algorithm. In: Proceedings of International Conference on Genetic Programming. Stanford, CA. pp. 380-385.
Zhou, G., McCalley, J. D. and Honavar, V. (1997). Power System Security Margin Prediction Using Radial Basis Function Networks. In: Proceedings of the 29th Annual North American Power Symposium. Laramie, Wyoming. October 13-14, 1997.
Balakrishnan, K. and Honavar, V. (1996). Analysis of Neurocontrollers Designed by Simulated Evolution. Proceedings of the International Conference on Neural Networks. Washington, D.C.
Balakrishnan, K. and Honavar, V. (1996). On Sensor Evolution in Robotics. Proceedings of the First International Conference on Genetic Programming, Stanford University, CA. pp. 455-460.
Balakrishnan, K. and Honavar, V. (1996). Some Experiments in Evolutionary Synthesis of Robotic Neurocontrollers In: Proceedings of the World Congress on Neural Networks. (WCNN'96) San Diego, CA. September 15-20, 96. pp. 1035-1040.
Chen, C-H. and Honavar, V. (1996). A Neural Network Architecture for High-Speed Database Query Processing. Microcomputer Applications. vol. 15, no. 1. pp. 7-13.
Mikler, A., Honavar, V. and Wong, J. (1996). Utility-Theoretic Heuristics for Intelligent Adaptive Routing in Large Communication Networks In: Proceedings of the Fourth International Conference on Telecommunication Systems. Nashville, TN. pp. 660-676.
Mikler, A., Honavar, V. and Wong, J. (1996). Analysis of Utility-Theoretic Heuristics for Intelligent Adaptive Network Routing. In:Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96). San Diego: AAAI Press. (1996). vol. 1, pp. 96-101.
Parekh, R. and Honavar, V. (1996). An Incremental, Interactive Algorithm for Regular Grammar Inference In: Proceedings of the Third International Colloquium on Grammar Inference (ICGI'96), Montpellier, France. September 24-27, 96. Lecture Notes in Computer Science, Springer-Verlag, vol. 1147, pp. 238-250.
Yang, J., Parekh, R. and Honavar, V. (1996). MTiling: A Constructive Neural Network Learning Algorithm for Multi-Category Pattern Classification. In: Proceedings of the World Congress on Neural Networks. (WCNN'96), San Diego, CA. September 15-20, 96. pp. 182-187.
Yang, J. and Honavar, V. (1996). A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers. In: Proceedings of the World Congress on Neural Networks. San Diego, CA. September 15-20, 96. pp. 223-228.
Balakrishnan, K. and Honavar, V. (1995) Properties of Genetic Representations of Neural Architectures. In: Proceedings of the World Congress on Neural Networks (WCNN'95). Washington, D.C. July 17-21, 1995. pp. 807-813.
Chen, C-H., Parekh, R., Yang, J., Balakrishnan, K. and Honavar, V. (1995). Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms. In: Proceedings of the World Congress on Neural Networks (WCNN'95). Washington, D.C. July 17-21, 1995. pp. 628-635.
Chen, C-H. and Honavar, V. (1995). A Neural Memory Architecture for Content as well as Address-Based Storage and Recall: Theory and Applications Connection Science. vol. 7. pp. 293-312.
Mikler, A., Wong J., and Honavar, V. (1995). Adaptive Heuristic Routing in Very Large High Speed Communication Networks Using Quo Vadis: Experimental Results. In: Proceedings of the Third International Conference on Telecommunication Systems. pp. 66-75. Nashville, TN.
Chen, C-H., and Honavar, V. (1994). Neural Network Automata. In: Proceedings of the World Congress on Neural Networks. pp. 470-477. San Diego, CA.
Mikler, A., Wong, J. and Honavar, V. (1994). Quo Vadis - A Framework for Intelligent Traffic Management. pp. 25-28. In: Proceedings of the International Conference on Intelligent Information Management Systems. Washington, D. C.
Honavar, V. and Uhr, L. (1993). Generative Learning structures for Generalized Connectionist Networks. Information Sciences 70 (1-2): 75-108.
Honavar, V. (1993). A Note on the Symbol Grounding Problem and its Solution. Invited Contribution. Think 2 41-43.
Honavar, V. (1993). Symbolic and Sub-Symbolic Learning for Vision: Some Possibilities. In: Proceedings of the AAAI Fall Symposium on Machine Learning in Computer Vision. pp. 162-166. Raleigh, North Carolina. (Also published as AAAI Tech. Rep. FS 93-04).
Janakiraman, J., and Honavar, V. (1993). Adaptive Learning Rate for Speeding up Learning in Backpropagation Networks. In: Proceedings of the SPIE Conference on Neural Networks. Orlando, Florida.
Janakiraman, J. and Honavar, V. (1993). Adaptive Learning Rate for Speeding up Gradient Descent Learning. In: Proceedings of the World Congress on Neural Networks. Portland, Oregon.
Mikler, A., Wong, J., and Honavar, V. (1993). Quo Vadis - A Framework for Adaptive Routing in Very Large High-Speed Communication Networks. In Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications. pp. 196-202. Princeton, New Jersey. Lawrence Erlbaum.
Parekh, R. and Honavar, V. (1993). Efficient Learning of Regular Languages Using Teacher-Generated Positive Samples and Learner-Generated Queries. In: Proceedings of the Fifth UNB AI Symposium. pp. 195-203. New Brunswick, Canada.
Thambu, P., Honavar, V., and Barta, T. (1993). Knowledge-base Consistency Maintenance in an Evolving Intelligent Advisory System. In: Proceedings of FLAIRS-93. Fort Lauderdale, Florida.
Yang, J. and Honavar, V. (1993). Hierarchical Representation Scheme for Three-Dimensional Object Recognition and Description. In: Proceedings of FLAIRS-93. Fort Lauderdale, Florida.
Balakrishnan, K. and Honavar, V. (1992). Faster Learning in Multi-layer Neural Networks by Eliminating Flat-spots. In: Proceedings of the Second International Conference on Artificial Neural Networks. Brighton, UK.
Balakrishnan, K. and Honavar, V. (1992). Faster Learning in Multi- Layer Networks by Handling Output-Layer Flat-Spots. In: Proceedings of International Joint Conference on Neural Networks. Beijing, China.
Honavar, V. (1992). Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns. In: Proceedings of International Joint Conference on Neural Networks. Beijing, China.
Honavar, V. (1992). Inductive Learning Using Generalized Distance Measures. Proceedings of the 1992 SPIE Conference on Adaptive and Learning Systems.
Honavar, V. (1992). Learning Parsimonious Representations of Three-Dimensional Shapes. In: NATO Advanced Research Workshop on Mathematical Representation of Shape. Driebergen, Netherlands.
Mikler, A., Honavar, V., and Wong, J. (1992). A Knowledge- Based Approach to Dealing With Uncertain and Incomplete Information in Communication Network Management. In: Proceedings of the First Canadian Workshop on Uncertainty Management: Theory and Practice. pp. 30-38. Vancouver, B. C., Canada.
Mikler, A., Honavar, V., and Wong, J. (1992). Simulating a Traveller: A Heuristic Approach to Routing in Large Communication Networks. In: Proceedings of the European Simulation Symposium. pp. 297-301. Dresden, Germany.
Parekh R., Balakrishnan, K., and Honavar, V. (1992). Empirical Comparison of Flat-Spot Elimination Techniques in Back-propagation Networks. In: Proceedings of Simtec/WNN92. pp. 463-468. Houston, Texas.
Spartz, R., and Honavar, V. (1992). Empirical Analysis of the Expected Source Values Rule. In: Proceedings of International Joint Conference on Neural Networks. Beijing, China.
Yang, J. and Honavar, V. (1991). Experiments with the Cascade-Correlation Algorithm. In: Proceedings of the International Joint Conference on Neural Networks . Singapore.
Yang, J., and Honavar, V. (1991). Experiments with the Cascade Correlation Algorithm. In: Proceedings of the Fourth UNB Artificial Intelligence Symposium. Fredericton, Canada. pp. 369-380.
Honavar, V. and Uhr, L. (1990). Successive Refinement of Multi-Resolution Internal Representations of the Environment in Connectionist Networks. In: Proceedings of the Second Conference on Neural Networks and Parallel-Distributed Processing.
Honavar, V. and Uhr, L. (1990). Coordination and Control Structures and Processes: Possibilities for Connectionist Networks. Journal of Experimental and Theoretical Artificial Intelligence 2: 277-302.
Honavar, V. and Uhr, L. (1989). Generation, Local Receptive Fields, and Global Convergence Improve Perceptual Learning in Connectionist Networks. In: Proceedings of the 1989 International Joint Conference on Artificial Intelligence, pp. 180-185. San Mateo, CA: Morgan Kaufmann.
Honavar, V. and Uhr, L. (1989). Brain-Structured Connectionist Networks that Perceive and Learn. Connection Science 1: 139-160.
Honavar, V. and Uhr, L. (1988). A Network of Neuron-Like Units That Learns To Perceive By Generation As Well As Reweighting Of Its Links. In: Proceedings of the 1988 Connectionist Models Summer School, San Mateo, CA: Morgan Kaufmann.

Book Chapters

Schade, M.M., Roberts, D.M., Honavar, V.G. and Buxton, O.M., 2023. Machine learning approaches in sleep and circadian research. In Encyclopedia of Sleep and Circadian Rhythms: Volume 1-6, Second Edition (pp. 53-62). Elsevier.

Bao, J., Slutzki, G., and Honavar, V. (2009). P-DL: A Semantic Importing Approach to Selective Knowledge Reuse in Modular Ontologies. In: Ontology Modularization. Parent, C., Spaccapietra, S., and Stuckenschmidt, H. (Ed). Berlin: Springer.
Honavar, V. and Caragea, D. (2008). Towards a Semantics-Enabled Infrastructure for Knowledge Acquisition from Distributed Data. In: Next Generation Data Mining. Kargupta, H. et al. (ed). Taylor and Francis.
Caragea, C. and Honavar, V. (2008). Machine Learning in Computational Biology. In: Encyclopedia of Database Systems, (Raschid, L., Editor), Springer.
Caragea, D., Cook, D., Wickham, H., and Honavar, V. (2008). Visual Methods for Examining SVM Classifiers. In: Visual Data Mining: Theory, Techniques, and Tools for Visual Analytics. Springer.
Caragea, D. and Honavar, V. (2008). Learning Classifiers from Distributed Data. In: Encyclopedia of Database Technologies and Applications, Ferraggine, V.E., Doorn, J.H., and Rivero, L.C. (Ed). New York: Idea Group.
Caragea, D. and Honavar, V. (2008). Learning Classifiers from Semantically Heterogeneous Data. In: Encyclopedia of Data Warehousing and Mining, Wang, J. (ed).
Pathak, J., Basu, S., Honavar, V. (2008). Assembling Composite Web Services from Autonomous Components. In: Emerging Artificial Intelligence Applications in Computer Engineering, Maglogiannis, I., Karpouzis, K., and Soldatos, J. (ed). IOS Press.
Honavar, V., Miller, L., and Wong, J. Distributed Knowledge Networks. In: Unifying Themes in Complex Systems (Ed. Bar-Yam, Y., and Minai, A.), Perseus Books 2004.
McCalley, J., Honavar, V., Zhang, Z., and Vishwanathan, V. Multiagent negotiation models for power system applications. In: Autonomous Systems and Intelligent Agents in Power System Control and Operation (Ed. Christian Rehtanz), Springer-Verlag 2003.
Balakrishnan, K. & Honavar, V. (2001). Experiments in Evolutionary Robotics. In: Advances in Evolutionary Synthesis of Intelligent Agents. Patel, M., Honavar, V. and Balakrishnan, K. (Ed). Cambridge, MA: MIT Press.
Caragea, D., Silvescu, A., and Honavar, V. (2001). Invited Chapter. Towards a Theoretical Framework for Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. In: Emerging Neural Architectures Based on Neuroscience. Berlin: Springer-Verlag.
Honavar, V. and Balakrishnan, K. (2001). Evolutionary Synthesis of Intelligent Agents. In: Evolutionary Synthesis of Intelligent Agents. Patel, M., Honavar, V. and Balakrishnan, K. (ed). Cambridge, MA: MIT Press.
Chen, C-H. & Honavar, V. (2000). A Neural Architecture for Information Retrieval and Query Processing. Invited chapter. In: Handbook of Natural Language Processing. Dale, Moisl, and Somers (Ed.) New York: Marcel Dekker.
Parekh, R. & Honavar, V. (2000). Automata Induction, Grammar Inference, and Language Acquisition. Invited chapter. In: Handbook of Natural Language Processing. Dale, Moisl & Somers (Ed). New York: Marcel Dekker.
Balakrishnan, K. & Honavar, V. (1999). Evolutionary Synthesis of Sensor Systems and Controllers. Invited chapter In: Evolutionary Computing Techniques in System Design Jain, L. (Ed.), New York: CRC Press.
Honavar, V., Parekh, R. and Yang, J. (1999). Structural Learning. Invited article. In: Encyclopedia of Electrical and Electronics Engineering, Webster, J. (Ed.), New York: Wiley.
Honavar, V., Parekh, R. and Yang, J. (1999). Machine Learning. Invited article. In: Encyclopedia of Electrical and Electronics Engineering, Webster, J. (Ed.), New York: Wiley.
Yang, J. and Honavar, V. (1998). Feature Subset Selection Using a Genetic Algorithm. Invited chapter. In: Feature Extraction, Construction, and Subset Selection: A Data Mining Perspective. Motoda, H. and Liu, H. (Ed.) New York: Kluwer. 1998.
Honavar, V. and Uhr, L. (1995). Integrating Symbol Processing and Connectionist Networks. Invited chapter. In: Intelligent Hybrid Systems. pp. 177-208. Goonatilake, S. and Khebbal, S. (Ed.) London: Wiley.
Honavar, V. (1994). Toward Learning Systems That Use Multiple Strategies and Representations. In: Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. pp. 615-644. Honavar, V. and Uhr, L. (Ed.) New York: Academic Press.
Honavar, V. (1994). Symbolic Artificial Intelligence and Numeric Artificial Neural Networks: Toward a Resolution of the Dichotomy. Invited chapter. In: Computational Architectures Integrating Symbolic and Neural Processes. pp. 351-388. Sun, R. and Bookman, L. (Ed.) New York: Kluwer.
Uhr, L., and Honavar, V. (1994). Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. In: Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. pp. xvii-xxxii. Honavar, V. and Uhr, L. (Ed). New York: Academic Press.

Invited Book Reviews

Honavar, V. (1992). Neural Network Design and the Complexity of Learning. Machine Learning 9 95-98.
Honavar, V. (1990). Parallel Distributed Processing: Implications for Psychology and Neurobiology. Connection Science.

Plenary Talks and Invited Lectures at Conferences

Invited Keynote Talk, From Big Data Analytics to Discovery Informatics. Conference on Complex Adaptive Systems, Washington DC. November 2012.
Invited Talk, Computational Prediction of Protein Interfaces and Interactions. Conference on Modeling Protein Interactions, Lawrence, Kansas, November 2012.
Invited Keynote Talk, Learning Predictive Models from Distributed Data. Conference on Intelligent Data Understanding, Boulder, CO, October 2012.
Honavar, V. (2009). Invited Keynote Talk, Chicago Colloquium on Digital Humanities and Computer Science, Chicago, November 2009.
Honavar, V. (2009). Invited Keynote Talk, Aligning Macromolecular Networks. Sixth International Biotechnology and Bioinformatics Symposium (BIOT 2009), Lincoln, Nebraska, October 2009.
Honavar, V. (2008). Invited Plenary Talk, Machine Learning in Bioinformatics, Annual Conference of the Italian Association for Artificial Intelligence (AI*IA 2008), Cagliari, Italy, September 2008.
Honavar, V. (2008). Keynote Talk, International Congress on Pervasive Computing and Management (ICPCM 2008), New Delhi, India, December 2008.
Honavar, V. (2008). Invited Talk, Telluride Meeting on Characterizing the Landscape From Biomolecules to Cellular Networks, Telluride, Colorado, July 2008.
Honavar, V. (2008). Invited Talk, Privacy‐preserving Reasoning, Semantic Technology Conference, San Jose, CA, USA, May 2008
Honavar, V. (2007). Keynote Talk, Computational Structural Bioinformatics Workshop, IEEE Conference on Bioinformatics and Biomedicine, Silicon Valley, 2007.
Honavar, V. (2007). Invited Talk, Making Biology and Medicine a Predictive Science. NSF Workshop on Biomedical Informatics. Oregon, 2007.
Honavar, V. (2007). Invited Talk, Knowledge Acquisition from Semantically Disparate Distributed Data. NSF Workshop on Next Generation Data Mining and Cyber‐Enabled Discovery, Baltimore, Maryland, 2007.
Honavar, V. (2007). Invited Lecture. On Selective Sharing and Reuse of Ontologies. Semantic Technologies Conference, San Jose, CA, May 2007.
Honavar, V. (2006). Keynote Talk, Semantic Web for Collaborative e-Science, International Conference on Intelligent Sensing and Information Processing, Bangalore, India.
Invited Lecture: Querying Semantically Heterogeneous Data Sources from a User's Point of View, Semantic Technologies Conference, San Jose, CA, USA, March 2006.
Honavar, V. Invited Plenary Lecture: Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed, Information Sources, Algorithmic learning theory (ALT 2005) and Discovery Science (DS 2005), Singapore, October 2005.
Honavar, V. and Caragea, D. Invited Lecture: Querying Semantically Heterogeneous Data Sources from a User's Point of View, Semantic Technology Conference, San Jose, CA, USA, March 2006.
Honavar, V. Plenary Talk, Data-Driven Discovery of Macromolecular Sequence-Structure-Function Relationships. International Conference on Intelligent System Design and Applications, 2003.
Honavar, V. Invited Talk, Agent-Based Distributed Intelligent Information Networks for Computational Inference and Knowledge Discovery in Bioinformatics. In: Workshop on Agents in Bioinformatics, Italy, 2002.
Honavar, V. Plenary Talk, Computational Discovery of Protein Sequence-Structure-Function Relationships, Diversity in Information Science and Technology, Nebraska EPSCOR Conference, 2002
Honavar, V. Keynote Address, Learning from Large, Distributed, Heterogeneous Data Sets. International Symposium on Artificial Intelligence (ISAI 2001), Kohlapur, India.
Honavar, V. Invited Talk, Distributed Intelligent Information Networks. Midwestern Conference on Artificial Intelligence and Cognitive Science, 2000.
Honavar, V. Invited Talk, Cumulative Learning in Open Environments. International Workshop on Current Computational Architectures Integrating Neural Networks and Neuroscience. Durham Castle, United Kingdom. 2000.
Honavar, V. Invited Talk, Distributed Knowledge Networks. Artificial Intelligence for Distributed Information Networks (AiDIN '99) Workshop held during the 1999 National Conference on Artificial Intelligence (AAAI 99), Orlando, Florida. July 1999.

Tutorials

Honavar, V. and Caragea, D. Tutorial: Collaborative Knowledge Acquisition from Semantically Disparate, Distributed Data Sources, 2006 International Symposium on Collaborative Technologies and Systems, Las Vegas, Nevada, USA, May 2006.
Honavar, V. and Caragea, D. Semantic Web Technologies for Collaborative Knowledge Acquisition, International Conference on Digital Information Management, Bangalore, India, December 2006.
Intelligent Agents and Multi-Agent Systems IEEE Conference on Evolutionary Computation (CEC), Washington, DC. 1999.
Intelligent Agents, Genetic Programming Conference, Madison, WI, 1998.
Computational Learning Theory, Genetic Programming Conference, Stanford, 1997.

Invited Colloquia, Distinguished Lectures

Distinguished Lecture, Computational Prediction of Protein Interfaces and Interactions, Georgia State University. January 2013.
Distinguished Lecture. From Big Data Analytics to Discovery Informatics. College of Information Science and Technology. Pennsylvania State University, December 2012.
Invited Colloquium, Computational Prediction of Protein Interfaces and Interactions, University of California Irvine, September 2012.
Invited Colloquium, Computational Prediction of Protein Interfaces and Interactions. University of North Texas, June 2012.
Invited Talk, Towards Infrastructure for Collaborative Discovery. ICiS Workshop on Integrating, Representing, and Reasoning over Human Knowledge, August 2010.
Invited Talk, Knowledge Acquisition from Semantically Disparate, Distributed Data. CISE (IIS), National Science Foundation, May 2010.
Honavar, V. (2009). Invited Talk, From Annotating Sequences to Aligning Networks. University Sixth Annual Computation and Informatics in Biology and Medicine Retreat, University of Wisconsin, Madison, October 2009.
Honavar, V. (2009). Invited Colloquium, Transforming Biology From a Descriptive Science into a Predictive Science, Indian Institute of Information Technology, Bangalore, India, January 2009.
Honavar, V. (2008). Invited Colloquium, Transforming Biology From a Descriptive Science into a Predictive Science: Predictive Models of Macromolecular Function and Interaction. Bioinformatics Center, University of Pune, India, December 2008.
Honavar, V. (2008). Invited Colloquium, Semantics-Enabled Infrastructure for Collaborative, Integrative e‐Science. School of Information Technology, Jawaharlal Nehru University, New Delhi, India, December 2008.
Honavar, V. (2008). Invited Talk, Computational Sciences. High Performance Computing Center, Jawaharlal Nehru University, New Delhi, India, December 2008.
Honavar, V. (2008). Invited Colloquium, Semantics-Enabled infrastructure for collaborative, integrative e‐science. Yahoo!, Bangalore, India, January 2008.
Honavar, V. (2007). Semantic Web for Collaborative Knowledge Acquisition. HP Research labs, Bangalore, India.
Honavar, V. (2006). Invited Colloquium, Algorithms and Software for Knowledge Acquisition from Semantically Heterogeneous, Distributed Data Sources. Dept. of Electrical and Computer Engineering. University of Iowa.
Honavar, V. (2006). Invited Colloquium, Algorithms and Software for Collaborative Discovery in Systems Biology. Dept. Biostatistics, Bioinformatics and Epidemiology. Medical University of South Carolina.
Honavar, V. (2005). Invited Talk, Algorithms and Software for Knowledge Acquisition from Semantically Heterogeneous, Distributed, Autonomous Information Sources. Google Research.
Honavar, V. Invited Talk, All Science is Computer Science. Iowa Undergraduate Consortium. Drake University, 2004.
Honavar, V. Invited Colloquium, Computational Discovery of Protein Sequence-Structure-Function Relationships: Bioinformatics Infrastructure and Sample Applications. University of Wisconsin-Madison Biostatistics and Medical Informatics Department. 2002.
Honavar, V. Algorithmic and Systems Approaches to Computer Assisted Knowledge Discovery from Biological Data. Iowa State University - University of Iowa Joint Workshop on Bioinformatics. November 3-4, 2000.
Honavar, V. Invited Talk, Neuromimetic Adaptive Autonomous Intelligent Systems. Institute for Computer Applications in Science and Engineering. NASA-Langley Research Center. Hampton, VA. September 28, 1999.
Honavar, V. Invited Colloquium, Kolmogorov Complexity and Computational Learning Theory: Some Emerging Connections and Recent Results. Center for Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, PA. 1998.
Honavar, V. Experiments in Evolutionary Robotics. Department of Mathematics and Computer Science, Grinnell College, Iowa. October 1996.
Honavar, V. Data Mining and Knowledge Discovery. Irish Life, Des Moines, Iowa. September 1996.
Honavar, V. Computational Models of Learning. Department of Electrical and Computer Engineering, Iowa State University, 1996.
Honavar, V. Experiments in Evolutionary Robotics. Neurosciences Seminar, Iowa State University, 1996.
Honavar, V. Knowledge Acquisition through Machine Learning. Principal Mutual, Des Moines, Iowa. January 1994.
Honavar, V. Panel on Learning in Knowledge-Based Systems. Second World Congress on Expert Systems. Lisbon, Portugal (1994).
Honavar, V. Panel on Hybrid Architectures for Intelligent Systems. Second World Congress on Expert Systems. Lisbon, Portugal (1994).
Honavar, V. Panel on Hybrid Intelligent Systems, World Congress on Neural Networks. San Diego, U.S.A. (1994).
Honavar, V. Generalized Connectionist Networks and Processes for Intelligent Systems. International Computer Science Institute, Berkeley, CA. (1990).
Honavar, V. Generative Learning Structures and Processes for Generalized Connectionist Networks. Cognitive and Learning Systems Laboratory, Siemens Research, Princeton, NJ. (1990).

Doctoral Dissertations

Dae-Ki Kang (2006). Abstraction, Aggregation and Recursion for Generating Accurate and Simple Classifiers. Doctoral Dissertation. Department of Computer Science, Iowa State University.
Changhui Yan (2005). Analysis and Computational Prediction of Protein-Protein and Protein-DNA Interfaces. Doctoral Dissertation. Department of Computer Science, Iowa State University.
Jun Zhang (2005). Learning Ontology Aware Classifiers. Doctoral Dissertation. Department of Computer Science, Iowa State University.
Doina Caragea (2004). Learning Classifiers From Distributed, Semantically Heterogeneous, Autonomous Data Sources. Doctoral Dissertation. Department of Computer Science, Iowa State University.
Yang, J. (1999). Adaptive Agents For Information Retrieval and Data-Driven Knowledge Acquisition. Doctoral Dissertation. Department of Computer Science. Iowa State University.
Balakrishnan, K. (1998). Biologically inspired computational structures and processes for intelligent agents and robots.. Doctoral Dissertation. Department of Computer Science, Iowa State University.
Parekh, R.G. (1998). Constructive Learning: Inducing Grammars and Neural Networks. Doctoral Dissertation. Department of Computer Science, Iowa State University.
Chen, C. (1998). Neural Architectures for Associative Memory, Syntax Analysis, Knowledge Representation and Inference. Doctoral Dissertation. Department of Computer Science, Iowa State University.
Mikler, R. (1995). Quo Vadis - A Framework for Intelligent Routing in Large Communication Networks. Doctoral Dissertation. Department of Computer Science. Iowa State University.
Honavar, V. (1990). Generative Learning Structures and Processes for Generalized Connectionist Networks. Doctoral Dissertation. Madison, WI: Computer Sciences Dept. University of Wisconsin-Madison.

Masters Theses and Projects

Charles Gieseler (2005). A Java Reinforcement Learning Module for the Recursive Porous Agent Simulation Toolkit. Masters Thesis, Department of Computer Science, Iowa State University.
Anna Atramentov (2003). Multi-Relational Decision Tree Learning Algorithm - Implementation and Experiments. Masters Thesis, Department of Computer Science, Iowa State University.
Hector Leiva. 2002. Thesis: MRDTL: A Multi-Relational Decision Tree Learning Algorithm. Masters Thesis.
Jaime Reinoso-Castillo. 2002. Thesis: Ontolgy-Driven Information Extraction and Integration from Autonomous, Heterogeneous, Distributed Data Sources -- A Federated Query-Centric Approach. Masters Thesis.
Xiaosi Zhang. 2002. Gene Expression Pattern Analysis. Masters Thesis.
Neeraj Koul. 2001. Thesis: Clustering with Semi-Metrics.
Rushi Bhatt. 2001. Thesis: Spatial Learning and Localization.
Dake Wang. 2001. Project: Automated Construction of Motif-based Decision Trees for Protein Function Classification.
Asok Tiyyagura. Project: Mutual Information Based Association Rule Mining.
Chen, F. (2000). Learning Information Extraction Patterns. Masters Thesis. Department of Computer Science, Iowa State University.
Sharma, T. (2000). An Agent Toolkit for Distributed Knowledge Networks. Masters Thesis. Department of Computer Science, Iowa State University.
Konsella, S. (1997). Trie Compaction Using a Genetic Algorithm. Masters project report. Department of Computer Science, Iowa State University.
Balakrishnan, K. (1993). Faster-Learning Approximations of Back-propagation by Handling Flat-Spots. Masters Project Report. Department of Computer Science. Iowa State University.
Janakiraman, J. (1993). Adaptive Learning Rate for Increasing Learning Speed in Backpropagation Networks. Masters Project Report. Department of Computer Science. Iowa State University.
Parekh, R.(1993). Efficient Learning of Regular Languages Using Teacher-Supplied Positive Examples and Learner-Generated Queries. Masters Project Report. Department of Computer Science. Iowa State University.
Thambu, P. (1993). Automated Knowledge base consistency maintenance in an evolving intelligent advisory system. Masters Thesis. Department of Computer Science. Iowa State University.
Spartz, R. (1992). Speeding Up Back-propagation Using Expected Source values. Masters Project Report. Iowa State University.
Honavar, V. (1984). Automated Analysis of Dark-Field Autoradiographs. Masters Thesis. Philadelphia, PA: Center For Image Processing and Pattern Recognition. Department of Electrical and Computer Engineering. Drexel University.

Honors Theses

Graves, D. (1992). Parallel Architectures for Artificial Intelligence. Senior Honors Project Report. Iowa State University.
Barsness, E. (1993). An Object-Oriented Implementation of Genetic Algorithms. Senior Honors Project Report. Iowa State University.

Workshop and Poster Presentations

Caragea, C., Sinapov, J., Silvescu, A., Dobbs, D., and Honavar, V. (2007). Glycosylation Site Prediction Using Machine Learning Approaches. RECOMB 2007. Poster presentation.
Gleeson, C., Hamilton, M., Lee, J-H., Caragea, C., Sander, J., Zaback, P., Terribilini, M., Li, X., Wu, F., Sinapov, J., El-Manzalawy, Y., Jernigan, R.L., Honavar, V., and Dobbs, D. (2007). Generating Models as a Platform for Comparing Functional and Structural Elements of Telomerase. Midwest Symposium on Computational Biology and Bioinformatics, 2007.
Terribilini, M., Sander, J., Caragea, C., Lee, J-H., Jernigan, R.L., Honavar, V. and Dobbs, D. (2007). Comparing Sequence vs. Structure-based Predictions of RNA Binding Sites in Proteins. Midwest Symposium on Computational Biology and Bioinformatics. 2007.
Towfic, F., Gemperline, D., Caragea, C., Wu, F., Dobbs, D., and Honavar, V. Prediction of RNA-Protein Interfaces Using Structural Features. Midwest Symposium on Computational Biology and Bioinformatics. 2007.
Towfic, F., Gemperline, D., Caragea, C., Wu, F., Dobbs, D., and Honavar, V. Structural Characterization of RNA-Binding Sites of Proteins: Preliminary Results. IEEE BIBM Computational Structural Bioinformatics Workshop, 2007.
Andorf, C., Dobbs, D., and Honavar, V. Potential Errors in Mouse Protein Gene Ontology Annotations Returned by AmiGO. Oral Presentation in: Gene Ontology Users Workshop, MGED, Seattle, Washington, September, 2006.
Andorf, C., Dobbs, D., and Honavar, V. (2006) Learning classifiers for assigning sequences to subcellular localization families. Intelligent Systems in Molecular Biology (ISMB 2006), Fortaleza, Brazil. Poster Presentation.
EL-Manzalawy, Y., Caragea, C., Dobbs, D., Honavar, V. (2006) On the quality of motifs for protein phosphorylation site prediction. Intelligent Systems in Molecular Biology (ISMB 2006), Fortaleza, Brazil. Poster Presentation.
Bao, J. and Honavar, V. (2005). Collaborative Package-Based Ontology Building and Usage. In: IEEE Workshop on Knowledge Acquisition from nowledge Acquisition from Distributed, Autonomous, Semantically Heterogeneous Data and Knowledge Sources. Held in conjunction with the IEEE International Conference on Data Mining (ICDM 2005), Houston, Tx.
Caragea, C., Caragea, D., and Honavar, V. (2005). Learning Support Vector Machine Classifiers from Distributed Data. Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI 2005).
Caragea, D. and Honavar, V. (2006). Knowledge Discovery from Disparate Earth Data Sources. Second NASA Data Mining Workshop: Issues and Applications in Earth Sciences. Poster Session. Pasadena, CA, May 23-24, 2006.
Pathak, J., Basu, S., and Honavar, V. (2006). Modeling Web Service Composition Using Symbolic Transition Systems. AAAI '06 Workshop on AI-Driven Technologies for Services-Oriented Computing (AI-SOC), Boston, MA, 2006.
J. Pathak, S. Basu, R. Lutz, and V. Honavar. (2006). MoSCoE: A Framework for Modeling Web Service Composition and Execution. IEEE Conference on Data Engineering Ph.D. Workshop, Atlanta, GA, 2006.
Sander, J., Fu, F. Terribilini, M., Townsend, J., Winfrey, R., Wright, D., Lee, J.J., Zaback, P., F. Wu, F., Honavar, V., Voytas, D. and Dobbs, D. (2006) Designing C2H2 Zinc Finger Proteins to Target Specific Sites in Genomic DNA. 10th Annual Pacific Symposium on Biocomputing (PSB 2006), Maui, Hawaii. Poster Presentation. Honavar, V. (1991). Language and Knowledge: Communication, Acquisition, and Evolution. Invited presentation in: Second International Workshop on Human and Machine Cognition. Perdido Key, Florida.
Honavar, V. (1991). Toward Integrated Models of Natural Language Evolution, Development, Acquisition, and Communication in Multi-Agent Environments. In: Powers, D. and Reeker, L. (Ed.) Proceedings of the AAAI Spring Symposium on Machine Learning of Natural Language and Ontogeny. (MLNLO '91) pp. 82-86. Kaiserslautern, Germany: German AI Centre (DFKI).
Honavar, V. (1990). Toward Generalized Connectionist Networks: An Integration of Symbolic and Sub-Symbolic Approaches to the Design of Intelligent Systems. In: AAAI-90 Workshop on the Integration of Symbolic and Neural Processes. Boston, MA.
Honavar, V. (1990). Generative Learning Algorithms for Connectionist Networks. In: NIPS-90 Post-Conference Workshop on Constructive and Destructive Learning Algorithms. Keystone, CO. Fedrigo, O., Naylor, G., and Honavar, V. (1999). A Gene-Specific DNA Chip for Exploring Molecular Evolutionary Change. In: RECOMB 99. Lyon, France.

Otherwise Unpublished Technical Reports

Balakrishnan, K. and Honavar, V. (1995). Evolutionary Design of Neural Architectures - A Preliminary Taxonomy and Guide to Literature. Available as: ISU CS-TR 95-01.
Honavar, V. (1989). Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence to Computational Models. Tech. Rep. 818. Madison, WI: Computer Sciences Dept. University of Wisconsin-Madison.

Pennsylvania State University

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