Haym Hirsh's Publications

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

2011:

Seyda Ertekin, Haym Hirsh, and Cynthia Rudin (2011).
Approximating the Wisdom of the Crowd.
Proceedings of the Second Workshop on Computational Social Science and the Wisdom of Crowds, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Sierra Nevada, Spain.

2008:

Haym Hirsh (2008).
Data Mining Research: Current Status and Future Opportunities.
Statistical Analysis and Data Mining.

2007:

Sarah Zelikovitz, William Cohen, and Haym Hirsh (2007).
Extending WHIRL with Background Knowledge for Improved Text Classification.
Information Retrieval, 10(1):35-67.

2006:

Alexander L. Strehl, Chris Mesterharm, Michael L. Littman, and Haym Hirsh (2006)
Experience-Efficient Learning in Associative Bandit Problems.
Proceedings of the 23rd International Conference on Machine Learning (ICML 2006).

2005:

Alex Borgida, Thomas J. Walsh, and Haym Hirsh (2005)
Towards Measuring Similarity in Description Logics.
Proceedings of the International Workshop on Description Logics (DL05).
Matthew Stone and Haym Hirsh (editors) (2005)
Artificial Intelligence: The Next Twenty-Five Years.
AI Magazine, 26(4):85-97.

2004:

Haym Hirsh, Nina Mishra, and Leonard Pitt (2004)
Version Spaces and the Consistency Problem.
Artificial Intelligence 156(2):115-138.

2003:

Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee, and Aynur A. Dayanik (2003).
Converting Numerical Classification into Text Classification.
Artificial Intelligence, 143(1):51-77, January 2003.
Sofus Macskassy and Haym Hirsh (2003).
Adding Numbers to Text Classification.
Proceedings of the Twelfth International Conference on Information and Knowledge Management (CIKM-2003)
Michael L. Littman, Thu Nguyen, Haym Hirsh, Eitan M. Fenson, and Richard Howard (2003).
Cost-sensitive fault remediation for autonomic computing.
Workshop on AI and Autonomic Computing: Developing a Research Agenda for Self-Managing Computer Systems.

2002:

Sarah Zelikovitz and Haym Hirsh (2002).
Integrating Background Knowledge into Nearest-Neighbor Text Classification.
Proceedings of the 6th European Conference on Case Based Reasoning. Springer Verlag. (Published version of invited talk.)

2001:

Chumki Basu, Haym Hirsh, William W. Cohen, and Craig Nevill-Manning (2001).
Technical Paper Recommendation: A Study in Combining Multiple Information Sources.
Journal of Artificial Intelligence Research, Volume 14, pages 231-252.
Sarah Zelikovitz and Haym Hirsh (2001).
Using LSI for Text Classification in the Presence of Background Text.
Proceedings of the Tenth International Conference on Information and Knowledge Management (CIKM-2001)
Sofus A. Macskassy, Haym Hirsh, Foster Provost, Ramesh Sankaranarayanan, and Vasant Dhar (2001).
Intelligent Information Triage.
Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2001).
Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee, Aynur A. Dayanik (2001).
Using Text Classifiers for Numerical Classification.
Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001).
Mark Schwabacher, Tom Ellman, and Haym Hirsh (2001).
Learning to Set Up Numerical Optimizations of Engineering Designs.
In Dan Braha, editor, Data Mining for Design and Manufacturing: Methods and Applications.
Kluwer Academic Publishers.
Sarah Zelkovitz and Haym Hirsh (2001).
Improving Text Classification with LSI Using Background Knowledge.
Working Notes of the Workshop on Text Learning: Beyond Supervision,
Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001).
Sofus A. Macskassy, Haym Hirsh, Foster Provost, Ramesh Sankaranarayanan, Vasant Dhar (2001).
Information Triage using Prospective Criteria.
Working Notes of the Workshop on Machine Learning, Information Retrieval and User Modeling,
Eighth International Conference on User Modeling (UM-2001).
Haym Hirsh and Steven Chien (editors) (2001).
Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence.
AAAI Press/MIT Press.
Robert Engelmore and Haym Hirsh (2001).
The Twelfth Innovative Applications of Artificial Intelligence Conference (IAAI-2001).
AI Magazine, 22(2):13-14.

2000:

Haym Hirsh, Chumki Basu, and Brian Davison (2000).
Learning to Personalize.
Communications of the ACM, August 2000, Vol. 43, No. 8, pp. 102-106.
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.
Sarah Zelikovitz and Haym Hirsh (2000).
Improving Short-Text Classification using Unlabeled Background Knowledge to Assess Document Similarity.
Proceedings of the Seventeenth International Conference on Machine Learning (ICML-2000).
Morgan Kaufmann Publishers.
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).
Sofus A. Macskassy, Aynur A. Dayanik, and Haym Hirsh (2000).
Information Valets for Intelligent Information Access.
Working Notes of the AAAI Spring Symposia Series on Adaptive User Interfaces (AUI-2000).
Robert Engelmore and Haym Hirsh (editors) (2000).
Proceedings of The Twelfth Innovative Applications of Artificial Intelligence Conference.
AAAI Press/MIT Press.
Haym Hirsh (editor) (2000)
Trends and Controversies: Genetic Programming.
IEEE Intelligent Systems, 15(3):74-84.
Marti A. Hearst and Haym Hirsh (editors) (2000)
AI's Greatest Trends and Controversies.
IEEE Intelligent Systems, 15(1):8-17.

1999:

Khaled Rasheed and Haym Hirsh (1999).
Learning to be Selective in Genetic-Algorithm-Based Design Optimization.
Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
Sofus A. Macskassy, Aynur A. Dayanik, and Haym Hirsh (1999).
EmailValet: Learning User Preferences for Wireless Email.
Working Notes of the Workshop on Learning about Users, and Working Notes of the Workshop on Machine Learning for Information Filtering,
Sixteenth International Joint Conference on Artificial Intelligence (IJCAI99).
Chumki Basu, Haym Hirsh, William Cohen, and Craig Nevill-Manning (1999).
Recommending Papers by Mining the Web.
Working Notes of the Workshop on Learning about Users, and Working Notes of the Workshop on Machine Learning for Information Filtering, Sixteenth International Joint Conference on Artificial Intelligence (IJCAI99).
Chumki Basu and Haym Hirsh (1999).
Learning User Models for Recommendation
Working Notes of the Workshop on Machine Learning for User Modeling, International Conference on User Modeling (UM99).
Sofus A. Macskassy, Aynur A. Dayanik, Haym Hirsh (1999).
EmailValet: Learning Email Preferences for Wireless Platforms.
Working Notes of the Workshop on Machine Learning for User Modeling, International Conference on User Modeling (UM99).
Daniel Kudenko and Haym Hirsh (1999).
Feature-Based Learners for Description Logics.
Proceedings of the International Workshop on Description Logics (DL99).
Haym Hirsh (editor) (1999)
Trends and Controversies: Playing with AI.
IEEE Intelligent Systems, 14(6):8-18.
Haym Hirsh (editor) (1999)
Trends and Controversies: A Quantum Leap for AI.
IEEE Intelligent Systems, 14(4):9-16.
Haym Hirsh (editor) (1999)
Trends and Controversies: Room Service, AI-Style.
IEEE Intelligent Systems, 14(2):8-19.

1998

Ronen Feldman, Ido Dagan, and Haym Hirsh (1998).
Mining Text Using Keyword Distributions.
Intelligent Information Systems, 10(3):281-300.
Mark Schwabacher, Tom Ellman, and Haym Hirsh (1998).
Learning to Set Up Numerical Optimizations of Engineering Designs.
Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 12(2):173-192.
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.
Sofus Macskassy, Arunava Banerjee, Brian Davison, and Haym Hirsh (1998).
Human Performance on Clustering Web Pages: A Preliminary Study.
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.
Chumki Basu, Haym Hirsh, and William W. Cohen (1998).
Recommendation as Classification: Using Social and Content-Based Information in Recommendation.
Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI98).
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.
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).
Ronen Feldman, Moshe Fresko, Haym Hirsh, Yonatan Aumann, Orly Liphstat, Yonatan Schler, and Martin Rajman (1998).
Knowledge Management: A Text Mining Approach.
Proceedings of the Second International Conference on Practical Aspects of Knowledge Management.
Brian D. Davison and Haym Hirsh (1998).
Predicting Sequences of User Actions.
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.
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.
Chumki Basu, Haym Hirsh, and William W. Cohen (1998).
In Working Notes of the Workshop on Recommender Systems.
Fifteenth National Conference on Artificial Intelligence (AAAI98).
AAAI Press.
Brian D. Davison and Haym Hirsh (1998).
Probabilistic Online Action Prediction.
Working Notes of the AAAI Spring Symposium on Intelligent Environments, pages 148-154.
AAAI Press.
Sofus A. Macskassy, Arunava Banerjee, Brian D. Davison, and Haym Hirsh (1998).
Human Performance on Clustering Web Pages.
Rutgers University Computer Science Department Technical Report DCS-TR-355.
Haym Hirsh (editor) (1998)
Trends & Controversies: Interactive Fiction.
IEEE Intelligent Systems 13(6):12-21.

1997

Ronen Feldman and Haym Hirsh (1997).
Exploiting Background Information in Knowledge Discovery from Text.
Intelligent Information Systems, 9(1):83-97.
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).
Brian D. Davison and Haym Hirsh (1997).
Toward An Adaptive Command Line Interface.
Proceedings of the Seventh International Conference on Human-Computer Interaction (HCI97).
Elsevier Science Publishers.
Haym Hirsh and Brian D. Davison (1997).
An Adaptive UNIX Command-Line Assistant.
Proceedings of the First International Conference on Autonomous Agents (Agents97).
Ronen Feldman and Haym Hirsh (1997).
Finding Associations in Collections of Text.
In R.S. Michalski, I. Bratko, and M. Kubat, editors, Machine Learning and Data Mining: Methods and Applications, pages 223-240.
John Wiley and Sons.
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.
Ronen Feldman, Amihood Amir, Y. Aumann, A. Zilberstein, and Haym Hirsh (1997).
Incremental Algorithms for Association Generation.
Proceedings of the 1st Pacific Asia Conference on Knowledge Discovery and Data Mining.
Brian D. Davison and Haym Hirsh (1997).
Experiments in UNIX Command Prediction. (Student Abstract).
Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI97)
Kwong Bor Ng, Haym Hirsh, Paul B. Kantor, David Loewenstern, and Chumki Basu, (1997).
Data Fusion of Machine-Learning Methods for the TREC5 Routing Task (and other work).
Proceedings of the Fifth Text REtrieval Conference (TREC-5), pages 477-488.
NIST Special Publication 500-238.
Haym Hirsh and Daniel Kudenko (1997).
Representing Sequences in Description Logics Using Suffix Trees.
1997 Workshop on Description Logics.
Brian D. Davison and Haym Hirsh (1997).
Experiments in UNIX Command Prediction.
Rutgers University Computer Science Department Technical Report ML-TR-41.
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.

1996

Marti Hearst and Haym Hirsh (editors) (1996).
Working Notes of the AAAI Spring Symposium on Machine Learning in Information Access.
AAAI Press.
Ronen Feldman and Haym Hirsh (1996).
Mining Associations in Text in the Presence of Background Knowledge.
Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD96), pages 343-346.
Mark Schwabacher, Thomas Ellman, Haym Hirsh, and Gerard Richter (1996).
Learning to Choose a Reformulation for Numerical Optimization of Engineering Designs.
Proceedings of the Artificial Intelligence in Design Conference (AID96).
Ido Dagan, Ronen Feldman, and Haym Hirsh (1996).
Keyword-Based Browsing and Analysis of Large Document Sets.
Proceedings of the Symposium on Document Analysis and Information Retrieval (SDAIR96), pages 191-208.
Haym Hirsh (1996).
Boundaries of Tractability for Artificial Intelligence.
Introduction to Special Issue, Annals of Mathematics and Artificial Intelligence.
Mark Schwabacher, Thomas Ellman, and Haym Hirsh (1996).
Inductive Learning for Engineering Design Optimization. (Research abstract).
Artificial Intelligence for Engineering Design, Analysis and Manufacturing (AIEDAM), 10:179-180.
Mark Schwabacher, Haym Hirsh, and Tom Ellman (1996).
Learning To Select Prototypes and Reformulations for Design.
Working Notes of the Workshop on Machine Learning in Design.
Artificial Intelligence in Design Conference (AID96).

1995

Mark Schwabacher, Tom Ellman, Haym Hirsh, and Gerard Richter (1995).
Learning When Reformulation is Appropriate for Iterative Design.
Working Notes of the Workshop on Machine Learning in Engineering.
Fourteenth International Joint Conference on Artificial Intelligence (IJCAI95).
(Also Rutgers University Computer Science Department Technical Report HPCD-TR-28.)
Arunava Banerjee, Haym Hirsh, and Thomas Ellman (1995).
Inductive Learning of Feature Tracking Rules for Scientific Visualization.
Working Notes of the Workshop on Machine Learning in Engineering.
Fourteenth International Joint Conference on Artificial Intelligence (IJCAI95).
(Also Rutgers University Computer Science Department Technical Report HPCD-TR-29.)
Mark Schwabacher, Haym Hirsh, and Thomas Ellman (1995).
Inductive Learning for Engineering Design Optimization.
Working Notes of the Workshop on Applying Machine Learning in Practice.
Fourteenth International Joint Conference on Artificial Intelligence (IJCAI95).
Mark Schwabacher, Tom Ellman, Haym Hirsh, and Gerard Richter (1995).
Learning When Reformulation is Appropriate for Iterative Design.
Working Notes of the Symposium on Abstraction, Reformulation, and Approximation.
Haym Hirsh, Thomas Ellman, Arunava Banerjee, David Drischel, Hongbing Yao, and Norman Zabusky (1995).
Reduced Model Formation for 2D Vortex Interactions Using Machine Learning.
Working Notes of the AAAI Spring Symposium on Systematic Methods of Scientific Discovery.
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.

1994

William W. Cohen and Haym Hirsh (editors) (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 Michiel Noordewier (1994).
Using Background Knowledge to Improve Inductive Learning: A Case Study in Molecular Biology.
IEEE Expert, 9(5):3-6.
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 Michiel Noordewier (1994).
Using Background Knowledge to Improve Inductive Learning of DNA Sequences.
Proceedings of the Tenth IEEE Conference on Artificial Intelligence for Applications (CAIA94), pages 351-357.
IEEE Computer Society Press.
Mark Schwabacher, Haym Hirsh, and Thomas Ellman (1994).
Inductive Learning of Prototype Selection Rules for Case-Based Iterative Design.
Proceedings of the Tenth IEEE Conference on Artificial Intelligence for Applications (CAIA94), pages 56-62.
IEEE Computer Society Press.
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.
Haym Hirsh (1994).
What Should a Graduate of AI-101 Be Expected to Know?
Working Notes of the AAAI Fall Symposium on Improving Instruction of Introductory Artificial Intelligence.
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).

1993

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.
Mark Schwabacher, Haym Hirsh, and Tom Ellman (1993).
Inductive Learning of Prototype-Selection Rules for Case-Based Iterative Design.
Working Notes of the Workshop on Artificial Intelligence in Design.
Thirteenth International Joint Conference on Artificial Intelligence (IJCAI93).
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.

1992

Haym Hirsh (editor) (1992).
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.

1991

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.

1990

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.

1989

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.

1988

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.

1987

Armand E. Prieditis, Thomas G. Dietterich, Haym Hirsh, Smadar T. Kedar-Cabelli, Richard V. Kempinski, Steven Minton, and Devika Subramanian (1987).
AAAI-86 Learning Papers: Topics, Summaries, and Trends.
Machine Learning, 2(1)83-96.
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.

1986

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.