Machine Learning, Volume 3

Volume 3, 1988

• Pat Langley: Machine Learning as an Experimental Science. 5-8 BibTeX
• Richard S. Sutton: Learning to Predict by the Methods of Temporal Differences. 9-44 BibTeX
• Robert J. Hall: Learning by Failing to Explain: Using Partial Explanations to Learn in Incomplete or Intractable Domains. 45-77 BibTeX
• Russell Greiner: A Review of Machine Learning at AAAI-87. 79-92 BibTeX
• David E. Goldberg, John H. Holland: Genetic Algorithms and Machine Learning. 95-99 BibTeX
• J. Michael Fitzpatrick, John J. Grefenstette: Genetic Algorithms in Noisy Environments. 101-120 BibTeX
• Kenneth DeJong: Learning with Genetic Algorithms: An Overview. 121-138 BibTeX
• George G. Robertson, Rick L. Riolo: A Tale of Two Classifier Systems. 139-159 BibTeX
• Lashon B. Booker: Classifier Systems that Learn Internal World Models. 161-192 BibTeX
• Richard K. Belew, Stephanie Forrest: Learning and Programming in Classifier Systems. 193-223 BibTeX
• John J. Grefenstette: Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms. 225-245 BibTeX
• Thomas G. Dietterich: News and Notes. 247-249 BibTeX

Volume 3, 1989

• Pat Langley: Toward a Unified Science of Machine Learning. 253-259 BibTeX
• Peter Clark, Tim Niblett: The CN2 Induction Algorithm. 261-283 BibTeX
• Glenn A. Iba: A Heuristic Approach to the Discovery of Macro-Operators. 285-317 BibTeX
• John Mingers: An Empirical Comparison of Selection Measures for Decision-Tree Induction. 319-342 BibTeX
• Stephen Jose Hanson, Malcolm Bauer: Conceptual Clustering, Categorization, and Polymorphy. 343-372 BibTeX
• Thomas G. Dietterich: News and Notes. 373-375 BibTeX