Haym Hirsh's Publications:
Science and Engineering

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

• 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.
• 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).
  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.
• 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.
• 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.
• 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).
• 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.
• Khaled Rasheed, Haym Hirsh, and Andrew Gelsey (1997).
  A Genetic Algorithm for Continuous Design Space Search.
  Artificial Intelligence in Engineering, 11(3):295-305.
• 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).
• 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.
• 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).
• 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).
• 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.
• 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.
• 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.
• 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).
• 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).
• 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 (1989).
  Incremental Version Space Merging: A General Framework for Concept Learning.
  Stanford University Computer Science Department Technical Report (Ph.D. Dissertation).
• 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.