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Machine Learning, Volume 1, 1986

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Volume 1, Number 1, 1986

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Pat Langley:
On Machine Learning. 5-10
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John E. Laird, Paul S. Rosenbloom, Allen Newell:
Chunking in Soar: The Anatomy of a General Learning Mechanism. 11-46
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Tom M. Mitchell, Richard M. Keller, Smadar T. Kedar-Cabelli:
Explanation-Based Generalization: A Unifying View. 47-80
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J. Ross Quinlan:
Induction of Decision Trees. 81-106
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Jan M. Zytkow, Herbert A. Simon:
A Theory of Historical Discovery: The Construction of Componential Models. 107-136

Volume 1, Number 2, 1986

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Pat Langley:
Editorial: The Terminology of Machine Learning. 141-144
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Gerald DeJong, Raymond J. Mooney:
Explanation-Based Learning: An Alternative View. 145-176
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Larry A. Rendell:
A General Framework for Induction and a Study of Selective Induction. 177-226
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Thomas G. Dietterich, Nicholas S. Flann, David C. Wilkins:
News and Notes. 227-242

Volume 1, Number 3, 1986

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Pat Langley:
Editorial: Human and Machine Learning. 243-248
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Bruce W. Porter, Dennis F. Kibler:
Experimental Goal Regression: A Method for Learning Problem-Solving Heuristics. 249-286
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Thomas G. Dietterich:
Learning at the Knowledge Level. 287-316
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Jeffrey C. Schlimmer, Richard H. Granger:
Incremental Learning from Noisy Data. 317-354
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Yves Kodratoff, Gheorghe Tecuci, Thomas G. Dietterich:
News and Notes. Mach. Learn. 1(3): 355-358 (1986)

Volume 1, Number 4, 1986

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Pat Langley, Ryszard S. Michalski:
Machine Learning and Discovery. 363-366
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Brian Falkenhainer, Ryszard S. Michalski:
Integrating Quantitative and Qualitative Discovery: The ABACUS System. 367-401
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Mieczyslaw M. Kokar:
Determining Arguments of Invariant Functional Descriptions. 403-422
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Donald Rose, Pat Langley:
Chemical Discovery as Belief Revision. 423-451
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Thomas G. Dietterich:
News and Notes. Mach. Learn. 1(4): 453-454 (1986)

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