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ORCIDMachine 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. Mach. Learn. 3: 95-99 (1988) - J. Michael Fitzpatrick, John J. Grefenstette:
Genetic Algorithms in Noisy Environments. 101-120 - Kenneth A. De Jong:
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. Mach. Learn. 3: 247-249 (1988)
Volume 3, 1989
- 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. Mach. Learn. 3: 373-375 (1989)
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