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Note: Links lead to the DBLP on the Web. Vasant Honavar Doina Caragea , Adrian Silvescu , Vasant Honavar: A Framework for Learning from Distributed Data Using Sufficient Statistics and Its Application to Learning Decision Trees. Int. J. Hybrid Intell. Syst. 1 (2): 80-89 (2004) Doina Caragea , Dianne Cook , Vasant Honavar: Towards Simple, Easy-to-Understand, yet Accurate Classifiers. ICDM 2003 : 497-500 Jun Zhang , Vasant Honavar: Learning from Attribute Value Taxonomies and Partially Specified Instances. ICML 2003 : 880-887 Anna Atramentov , Hector Leiva , Vasant Honavar: A Multi-relational Decision Tree Learning Algorithm - Implementation and Experiments. ILP 2003 : 38-56 Xiangyun Wang , Diane Schroeder , Drena Dobbs , Vasant Honavar: Automated data-driven discovery of motif-based protein function classifiers. Inf. Sci. 155 (1-2): 1-18 (2003) Guy G. Helmer , Johnny S. Wong , Vasant Honavar, Les Miller , Yanxin Wang : Lightweight agents for intrusion detection. Journal of Systems and Software 67 (2): 109-122 (2003) H. John Caulfield , Shu-Heng Chen , Heng-Da Cheng , Richard J. Duro , Vasant Honavar, Etienne E. Kerre , Mi Lu , Manuel Grana Romay , Timothy K. Shih , Dan Ventura , Paul P. Wang , Yuanyuan Yang : Proceedings of the 6th Joint Conference on Information Science, March 8-13, 2002, Research Triangle Park, North Carolina, USA JCIS / Association for Intelligent Machinery, Inc. 2002 William B. Langdon , Erick Cantú-Paz , Keith E. Mathias , Rajkumar Roy , David Davis , Riccardo Poli , Karthik Balakrishnan , Vasant Honavar, Günter Rudolph , Joachim Wegener , Larry Bull , Mitchell A. Potter , Alan C. Schultz , Julian F. Miller , E. Burke , Natasa Jonoska : GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, New York, USA, 9-13 July 2002 Morgan Kaufmann 2002 Xiangyun Wang , Diane Schroeder , Drena Dobbs , Vasant Honavar: Data-Driven Discovery of Protein Function Classifiers: Decision Trees Based on MEME Motifs outperform PROSITE Patterns and Profiles on Peptidase Families. JCIS 2002 : 1193-1199 Carson M. Andorf , Drena Dobbs , Vasant Honavar: Discovering Protein Function Classification Rules from Reduced Alphabet Representations of Protein Sequences. JCIS 2002 : 1200-1206 Jun Zhang , Adrian Silvescu , Vasant Honavar: Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction. SARA 2002 : 316-323 Guy G. Helmer , Johnny S. Wong , Mark Slagell , Vasant Honavar, Les Miller , Robyn R. Lutz : A Software Fault Tree Approach to Requirements Analysis of an Intrusion Detection System. Requir. Eng. 7 (4): 207-220 (2002) Doina Caragea , Adrian Silvescu , Vasant Honavar: Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. Emergent Neural Computational Architectures Based on Neuroscience 2001 : 547-559 Doina Caragea , Dianne Cook , Vasant Honavar: Gaining insights into support vector machine pattern classifiers using projection-based tour methods. KDD 2001 : 251-256 Robi Polikar , L. Upda , S. S. Upda , Vasant Honavar: Learn++: an incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C 31 (4): 497-508 (2001) Vasant Honavar, Colin de la Higuera : Introduction. Machine Learning 44 (1/2): 5-7 (2001) Rajesh Parekh , Vasant Honavar: Learning DFA from Simple Examples. Machine Learning 44 (1/2): 9-35 (2001) Doina Caragea , Adrian Silvescu , Vasant Honavar: Incremental and Distributed Learning with Support Vector Machines. AAAI/IAAI 2000 : 1067 Rajesh Parekh , Vasant Honavar: On the Relationship between Models for Learning in Helpful Environments. ICGI 2000 : 207-220 Mokdong Chug , Vasant Honavar: A Negotiation Model in Agent-mediated Electronic Commerce. ISMSE 2000 : 403-410 Wolfgang Banzhaf , Jason M. Daida , A. E. Eiben , Max H. Garzon , Vasant Honavar, Mark J. Jakiela , Robert E. Smith : Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), 13-17 July 1999, Orlando, Florida, USA Morgan Kaufmann 1999 Guy G. Helmer , Johnny S. Wong , Vasant Honavar, Les Miller : Feature Selection Using a Genetic Algorithm for Intrusion Detection. GECCO 1999 : 1781 Rajesh Parekh , Vasant Honavar: Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples. ICML 1999 : 298-306 Jihoon Yang , Rajesh Parekh , Vasant Honavar, Drena Dobbs : Data-Driven Theory Refinement Using KBDistAl . IDA 1999 : 331-342 Jihoon Yang , Rajesh Parekh , Vasant Honavar: DistAl: An inter-pattern distance-based constructive learning algorithm. Intell. Data Anal. 3 (1): 55-73 (1999) Vasant Honavar, Giora Slutzki : Grammatical Inference, 4th International Colloquium, ICGI-98, Ames, Iowa, USA, July 12-14, 1998, Proceedings Springer 1998 Rajesh Parekh , Codrin Nichitiu , Vasant Honavar: A Polynominal Time Incremental Algorithm for Learning DFA. ICGI 1998 : 37-49 Jihoon Yang , Vasant Honavar: Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems 13 (2): 44-49 (1998) Rajesh Parekh , Vasant Honavar: Learning DFA from Simple Examples. ALT 1997 : 116-131 Armin R. Mikler , Johnny S. Wong , Vasant Honavar: Quo Vadis - A Framework for Intelligent Routing in Large Communication Networks. Journal of Systems and Software 37 (1): 61-73 (1997) Armin R. Mikler , Vasant Honavar, Johnny S. Wong : Analysis of Utility-Theoretic Heuristics for Intelligent Adaptive Network Routing. AAAI/IAAI, Vol. 1 1996 : 96-101 Karthik Balakrishnan , Vasant Honavar: Experiments in Evolutionary Synthesis of Robotic Neurocontrollers. AAAI/IAAI, Vol. 2 1996 : 1378 Rajesh Parekh , Vasant Honavar: An Incremental Interactive Algorithm for Regular Grammar Inference. AAAI/IAAI, Vol. 2 1996 : 1397 Rajesh Parekh , Jihoon Yang , Vasant Honavar: Constructive Neural Network Learning Algorithms. AAAI/IAAI, Vol. 2 1996 : 1398 Rajesh Parekh , Vasant Honavar: An incremental interactive algorithm for grammar inference. ICGI 1996 : 238-249 Vasant Honavar: Neural Network Design and the Complexity of Learning (Book Review). Machine Learning 9 : 95-98 (1992) Vasant Honavar, Leonard Uhr : Generation, Local Receptive Fields and Global Convergence Improve Perceptual Learning in Connectionist Networks. IJCAI 1989 : 180-185 1 [ 28 ] 2 [ 34 ] 3 [ 6 ] [ 30 ] 4 [ 17 ] 5 [ 30 ] 6 [ 30 ] 7 [ 30 ] 8 [ 20 ] [ 24 ] [ 25 ] [ 36 ] [ 37 ] 9 [ 31 ] 10 [ 31 ] 11 [ 31 ] 12 [ 18 ] 13 [ 24 ] [ 36 ] 14 [ 17 ] 15 [ 30 ] 16 [ 14 ] [ 28 ] [ 29 ] [ 33 ] 17 [ 31 ] 18 [ 17 ] 19 [ 17 ] 20 [ 16 ] [ 26 ] [ 32 ] 21 [ 22 ] 22 [ 17 ] 23 [ 30 ] 24 [ 31 ] 25 [ 30 ] 26 [ 34 ] 27 [ 31 ] 28 [ 26 ] 29 [ 30 ] 30 [ 7 ] [ 8 ] 31 [ 30 ] 32 [ 16 ] [ 26 ] [ 32 ] 33 [ 11 ] 34 [ 3 ] [ 4 ] [ 5 ] [ 9 ] [ 11 ] [ 13 ] [ 14 ] [ 15 ] [ 19 ] [ 21 ] 35 [ 30 ] 36 [ 23 ] 37 [ 30 ] 38 [ 31 ] 39 [ 30 ] 40 [ 30 ] 41 [ 29 ] [ 33 ] 42 [ 30 ] 43 [ 31 ] 44 [ 20 ] [ 25 ] [ 27 ] [ 37 ] 45 [ 26 ] 46 [ 12 ] 47 [ 17 ] 48 [ 1 ] 49 [ 23 ] 50 [ 23 ] 51 [ 31 ] 52 [ 31 ] 53 [ 29 ] [ 33 ] 54 [ 32 ] 55 [ 30 ] 56 [ 7 ] [ 8 ] [ 16 ] [ 26 ] [ 32 ] 57 [ 4 ] [ 10 ] [ 13 ] [ 14 ] 58 [ 31 ] 59 [ 27 ] [ 35 ]  ©2004 Association for Computing Machinery |
