Haym Hirsh's Publications:
Learning from Sequential Data

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

• 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).
• 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).
  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).
• 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 and Daniel Kudenko (1997).
  Representing Sequences in Description Logics Using Suffix Trees.
  1997 Workshop on Description Logics.
• 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 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.
• 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).