- Learnability of Description Logics William W. Cohen
- 4 Imposing Bounds on the Number of Categories for Incremental Concept
- In Proceedings IEEE Conference on AI for Applications, March, 1994
- Appears in Proceedings of the 15 th International Conference on Machine Learning, Morgan Kaufmann, 1998, 574578.
- Maximum A Posteriori Classification of DNA Structure from Sequence Information
- Feature Generation for Sequence Categorization Daniel Kudenko and Haym Hirsh
- In Proceedings of the 1998 AAAI/ICML Workshop "Predicting the Future: AI Approaches to TimeSeries Analysis" Predicting Sequences of User Actions
- In Proceedings Tenth International Conference on Machine Learning, 1993 Learning DNF Via Probabilistic Evidence Combination
- In Proceedings AAAI92 PolynomialTime Learning with Version Spaces
- Joins that Generalize: Text Classification Using WHIRL William W. Cohen
- In Proceedings 7th Annual Workshop on Space Operations, Applications and Research, 1993 TWO FRAMEWORKS FOR INTEGRATING KNOWLEDGE IN INDUCTION
- Improving Text Classification with LSI Using Background Knowledge Sarah Zelikovitz and Haym Hirsh
- In Proceedings HCI97 1 Toward An Adaptive Command Line Interface
- Mining Associations in Text in the Presence of Background Knowledge
- Artificial Intelligence, 143(1):51-77, 2003. Converting Numerical Classification into Text
- In Proceedings of the 1997 International Conference on Genetic Algorithms Using CaseBased Learning to Improve
- Towards Measuring Similarity in Description Logics Alex Borgida Thomas J. Walsh Haym Hirsh
- Recommendation as Classification: Using Social and ContentBased Information in Recommendation
- Improving ShortText Classification Using Unlabeled Background Knowledge to Assess Document Similarity
- In Proceedings AAAI92 Computing Least Common Subsumers
- Integrating Background Knowledge into NearestNeighbor Text Classification
- Recommending Papers by Mining the Web Chumki Basu yz Haym Hirsh y William W. Cohen x Craig NevillManning y
- Representing Sequences in Description Logics Haym Hirsh and Daniel Kudenko
- In Proceedings IEEE Conference on AI for Applications, March, 1994
- Version Spaces Without Boundary Sets hirsh@cs.rutgers.edu
- In Proceedings AAAI94 Bootstrapping TrainingData Representations for Inductive Learning
- Learning to Predict Rare Events in Event Sequences Gary M. Weiss +
- In Proceedings AAAI92 Classifier Learning from Noisy Data
