Machine Learning, Volume 3

Volume 3, 1988

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

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