Machine Learning, Volume 1, 1986

Volume 1, Number 1, 1986

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

Volume 1, Number 2, 1986

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

Volume 1, Number 3, 1986

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

Volume 1, Number 4, 1986

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

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