Haym Hirsh's Publications:
Foundations

Some papers can be viewed by clicking on their titles.
(A few more still need to be put on-line.)

• Khaled Rasheed and Haym Hirsh (2000).
  Informed Operators: Speeding Up Genetic-Algorithm-Based Design Optimization Using Reduced Models.
  Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'2000).
  
• Gary M. Weiss and Haym Hirsh (2000).
  A Quantitative Study of Small Disjuncts.
  Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000).
  AAAI Press/MIT Press.
• Gary M. Weiss and Haym Hirsh (2000).
  Learning to Predict Extremely Rare Events.
  Working Notes of the Workshop on Learning from Imbalanced Data Sets,
  The Seventeenth National Conference on Artificial Intelligence (AAAI-2000).
• Khaled Rasheed and Haym Hirsh (1999).
  Learning to be Selective in Genetic-Algorithm-Based Design Optimization.
  Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
• William W. Cohen and Haym Hirsh (1998).
  Joins that Generalize: Text Classification Using WHIRL.
  Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD98).
  AAAI Press/MIT Press.
• Gary M. Weiss and Haym Hirsh (1998).
  Learning to Predict Rare Events in Categorical Times-Series Data.
  Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD98).
  AAAI Press/MIT Press.
• Gary M. Weiss and Haym Hirsh (1998).
  Learning to Predict Rare Events in Categorical Times-Series Data.
  Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD98).
  AAAI Press/MIT Press.
• Daniel Kudenko and Haym Hirsh (1998).
  Feature Generation for Sequence Categorization.
  Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI98).
  AAAI Press/MIT Press.
• Gary M. Weiss and Haym Hirsh (1998).
  The Problem with Noise and Small Disjuncts.
  Proceedings of the Fifteenth International Conference on Machine Learning (ICML98).
  Morgan Kaufmann Publishers.
• Gary M. Weiss and Haym Hirsh (1998).
  Learning to Predict Rare Events in Categorical Time-Series Data.
  Working Notes of the Joint Workshop on Predicting the Future: AI Approaches to Time Series Analysis, Fifteenth National Conference on Artificial Intelligence (AAAI98)/Fifteenth International Conference on Machine Learning (ICML98).
  AAAI Press.
• David Loewenstern, Helen Berman, and Haym Hirsh (1998).
  Maximum A Posteriori Classification of DNA Structure from Sequence Information.
  Proceedings of the Pacific Symposium on Biocomputing (PSB98).
• Khaled Rasheed, Haym Hirsh, and Andrew Gelsey (1997).
  A Genetic Algorithm for Continuous Design Space Search.
  Artificial Intelligence in Engineering, 11(3):295-305.
• Haym Hirsh and Daniel Kudenko (1997).
  Representing Sequences in Description Logics.
  Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI97).
  AAAI Press/MIT Press.
• Haym Hirsh, Nina Mishra, and Leonard Pitt (1997).
  Version Spaces Without Boundary Sets.
  Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI97).
  AAAI Press/MIT Press.
• Khaled Rasheed and Haym Hirsh (1997).
  Using Case-Based Learning to Improve Genetic-Algorithm-Based Design Optimization.
  Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA97).
• Leon Shklar and Haym Hirsh (1997).
  Imposing Bounds on the Number of Categories for Incremental Concept Formation.
  In R. Greiner, T. Petsche, and S.J. Hanson, editors, Computational Learning Theory and Natural Learning Systems, Volume IV, pages 36-49.
  MIT Press.
• Haym Hirsh and Daniel Kudenko (1997).
  Representing Sequences in Description Logics Using Suffix Trees.
  1997 Workshop on Description Logics.
• Khaled Rasheed and Haym Hirsh (1997).
  Guided Crossover: A New Operator for Genetic Algorithm Based Optimization.
  Rutgers University Computer Science Department Technical Report HPCD-TR-50.
• Haym Hirsh (1996).
  Boundaries of Tractability for Artificial Intelligence.
  Introduction to Special Issue, Annals of Mathematics and Artificial Intelligence.
• David Loewenstern, Haym Hirsh, Peter Yianilos, and Michiel Noordewier (1995).
  DNA Sequence Classification Using Compression-Based Induction.
  Rutgers University Computer Science Department Technical Report LCSR-TR-240, DIMACS Technical Report 95-04.
• William W. Cohen and Haym Hirsh (1994).
  Machine Learning: Proceedings of the Eleventh International Conference.
  Morgan Kaufmann Publishers.
• William W. Cohen and Haym Hirsh (1994).
  The Learnability of Description Logics with Equality Constraints.
  Machine Learning, 17(2):169-199.
• Haym Hirsh (1994).
  Generalizing Version Spaces.
  Machine Learning, 17(1):5-45.
• Haym Hirsh and Nathalie Japkowicz (1994).
  Bootstrapping Training Data Representations for Inductive Learning: A Case Study in Molecular Biology.
  Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI94), pages 639-644.
  AAAI Press/MIT Press.
• William W. Cohen and Haym Hirsh (1994).
  Learning the CLASSIC Description Logic: Theoretical and Experimental Results.
  Proceedings of the Fourth International Conference on Principles of Knowledge Representation and Reasoning (KR94), pages 121-133.
• Haym Hirsh and William W. Cohen (1994).
  Learning from Data with Bounded Inconsistency: Theoretical and Experimental Results.
  In S. Hanson, G. Drastal, and R. Rivest, editors, Computational Learning Theory and Natural Learning Systems, Constraints and Prospects, pages 355-380.
  MIT Press.
• Nathalie Japkowicz and Haym Hirsh (1994).
  Towards a Bootstrapping Approach to Constructive Induction.
  Working Notes of the Workshop on Constructive Induction and Change of Representation, pages 27-32.
  Eleventh International Conference on Machine Learning (ICML94).
• Steven W. Norton and Haym Hirsh (1993).
  Learning DNF Via Probabilistic Evidence Combination.
  Proceedings of the Tenth International Conference on Machine Learning (ICML93), pages 220-227.
  Morgan Kaufmann Publishers.
• James Crawford and Haym Hirsh (1993).
  AAAI 1993 Spring Symposium Report: AI and NP-Hard Problems.
  AI Magazine, 14(4):32-34.
• Paul S. Rosenbloom, Haym Hirsh, William W. Cohen, and Benjamin D. Smith (1993).
  Two Frameworks for Integrating Knowledge in Induction.
  Proceedings of the Seventh Annual Workshop on Space Operations, Applications and Research, pages 226-233.
  NASA Conference Publication 3240. Working Notes of the AAAI Spring Symposium on Artificial Intelligence and NP-Hard Problems.
  AAAI Press.
• William W. Cohen, Alex Borgida, and Haym Hirsh (1992).
  Computing Least Common Subsumers in Description Logics.
  Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI92), pages 754-760.
  AAAI Press/MIT Press.
• Haym Hirsh (1992).
  Polynomial-Time Learning with Version Spaces.
  Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI92), pages 117-122.
  AAAI Press/MIT Press.
• Steven W. Norton and Haym Hirsh (1992).
  Classifier Learning from Noisy Data as Probabilistic Evidence Combination.
  Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI92), pages 141-146.
  AAAI Press/MIT Press.
• William W. Cohen and Haym Hirsh (1992).
  Learnability of Description Logics.
  Proceedings of the Fifth Annual Workshop on Computational Learning Theory (COLT92), pages 116-127.
  ACM Press.
• Haym Hirsh (1992).
  The Computational Complexity of the Candidate-Elimination Algorithm.
  Rutgers University Computer Science Department Technical Report ML-TR-36.
• Haym Hirsh (1991).
  Theoretical Underpinnings of Version Spaces.
  Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (IJCAI91), pages 665-670.
  Morgan Kaufmann Publishers.
• Steven W. Norton and Haym Hirsh (1991).
  Classifier Learning from Noisy Data as Reasoning Under Uncertainty.
  Rutgers University Computer Science Department Technical Report ML-TR-34.
• Haym Hirsh (1990).
  Incremental Version-Space Merging: A General Framework for Concept Learning.
  Foreword by Tom M. Mitchell.
  Kluwer Academic Publishers.
• Derek Sleeman, Haym Hirsh, Ian Ellery, and In-Yung Kim (1990).
  Extending Domain Theories: Two Case Studies in Student Modeling.
  Machine Learning, 5(1):11-37.
• Haym Hirsh (1990).
  Incremental Version-Space Merging.
  Proceedings of the Seventh International Conference on Machine Learning (ICML90), pages 330-338.
  Morgan Kaufmann Publishers.
• Haym Hirsh (1990).
  Learning from Data with Bounded Inconsistency.
  Proceedings of the Seventh International Conference on Machine Learning (ICML90), pages 32-39.
  Morgan Kaufmann Publishers.
• Haym Hirsh (1990).
  Conditional Operationality and Explanation-Based Generalization.
  In R. Michalski and Y. Kodratoff, editors, Machine Learning: An Artificial Intelligence Approach, Volume III, pages 383-395.
  Morgan Kaufmann Publishers.
• Haym Hirsh (1990).
  Knowledge As Bias.
  In D.P. Benjamin, editor, Change of Representation and Inductive Bias, pages 209-221.
  Kluwer Academic Publishers.
• Haym Hirsh (1990).
  Overgenerality in Explanation-Based Generalization.
  In P. Brazdil and K. Konolige, editors, Machine Learning, Meta-Reasoning, and Logics, pages 121-134.
  Kluwer Academic Publishers.
• Haym Hirsh (1989).
  Incremental Version Space Merging: A General Framework for Concept Learning.
  Stanford University Computer Science Department Technical Report (Ph.D. Dissertation).
• Haym Hirsh (1989).
  Combining Empirical and Analytical Learning with Version Spaces.
  Proceedings of the Sixth International Machine Learning Workshop, pages 29-33.
  Morgan Kaufmann Publishers.
• Scott H. Clearwater, Tze-Pin Cheng, Haym Hirsh, and Bruce G. Buchanan (1989).
  Incremental Batch Learning.
  Proceedings of the Sixth International Machine Learning Workshop, pages 366-370.
  Morgan Kaufmann Publishers.
• Mellisa P. Chase, Monte Zweben, Richard L. Piazza, John D. Burger, Paul P. Maglio, and Haym Hirsh (1989).
  Approximating Learned Search Control Knowledge.
  Proceedings of the Sixth International Machine Learning Workshop, pages 218-220.
  Morgan Kaufmann Publishers.
• Haym Hirsh (1988).
  Reasoning about Operationality for Explanation-Based Learning.
  Proceedings of the Fifth International Conference on Machine Learning (ICML88), pages 214-220.
  Morgan Kaufmann Publishers.
• Haym Hirsh (1988).
  Empirical Techniques for Repairing Imperfect Theories.
  Working Notes of the AAAI Spring Symposium on Explanation-Based Learning, pages 57-61.
• Haym Hirsh (1987).
  Explanation-Based Generalization in a Logic-Programming Environment.
  Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI87), pages 221-227.
  Morgan Kaufmann Publishers.
• Haym Hirsh and Derek Sleeman (1986).
  Inference of Incorrect Operators.
  In T. Mitchell, J. Carbonell, and R. Michalski, editors, Machine Learning: A Guide to Current Research, pages 91-97.
  Kluwer Academic Publishers.