[8] Machine Learning

General:

   J. G. Carbonell, editor, "Machine Learning: Paradigms and Methods", MIT
   Press, Cambridge, MA 1990.

   Jude W. Shavlik and Thomas D. Dietterich, editors, "Readings in
   Machine Learning", Morgan Kaufmann Publishers, 1990, 853 pages.
   ISBN 1-55860-143-0, $49.95.

   Tom Mitchell, Jaime G. Carbonell, and Ryszard S. Michalski,
   "Machine Learning: A guide to current research", Kluwer Academic
   Publishers, Boston, 1986. [A bit out of date.]

   Alan Hutchinson, "Algorithmic Learning", Oxford University Press,
   Oxford, England, 1994. 434 pages, ISBN 0-19-853848-0 paper (27.50
   Sterling), ISBN 0-19-853766-2 hardcover (55.00 Sterling). Corrections and
   additions are available by anonymous ftp from 
      dcs.kcl.ac.uk:/ftp/pub/alg-learn/ [137.73.8.10]

   [See also the article on Machine Learning from the Encyclopedia of
    Artificial Intelligence, pages 464-485.]

Decision Trees:

   Quinlan, J. Ross, "Induction of Decision Trees", Machine Learning
   1(1):81-106, 1986.  

   Quinlan, J. Ross, "C4.5: Programs for Machine Learning", Morgan Kaufmann
   Publishers, 1992. ISBN 1-55860-238-0. $44.95 US, $49.45 International.
   For a slight additional charge ($25), the book comes with software (ISBN
   1-55860-240-2). For software only, (ISBN 1-55860-239-9) $34.95 US,
   $38.45 International.

Probabilistic Clustering:

   Fisher, D.H., "Knowledge Acquisition Via Incremental Conceptual
   Clustering", Machine Learning 2:139-172, 1987. (Probabilistic
   clustering methods.) 

   Clancey, W.J., "Classification Problem Solving", Proceedings of the
   National Conference on Artificial Intelligence, 49-55, Los Altos, CA,
   Morgan Kaufmann. 1984.

Version Spaces:

   Tom M. Mitchell, "Generalization as Search", Artificial Intelligence
   18:203-226, 1982.

Machine Discovery:

   Langley, P., and Zytkow, J. M., "Data-driven approaches to empirical
   discovery", Artificial Intelligence 40:283-312, 1989.

   Langley, P., Simon, H.A., Bradshaw, G.L., and Zytkow, J.M.,
   "Scientific Discovery: Computational Explorations of the Creative
   Processes", MIT Press, Cambridge, MA, 1987.

   Langley, P., Simon, H.A. and Bradshaw, G.L., "Heuristics for
   Empirical Discovery", in L. Bolc, editor, Computational Models
   of Learning, Springer-Verlag, 1987. Also appears as CMU CS
   Tech Report CMU-CS-84-14.

Chunking:

   Laird J.E., Rosenbloom, P.S. and Newell, A., "Chunking in SOAR: The
   Anatomy of a General Learning Mechanism", Machine Learning
   1:1-46, 1986. 

Explanation-Based Learning:

   Mitchell, Tom M., Keller, R. M., and Kedar-Cabelli, S. T., 
   "Explanation-based learning: A unified view", Machine Learning
   1:47-80, 1986.

Derivational Analogy:

   Carbonell, J. G., "Derivational analogy: A theory of
   reconstructive problem solving and expertise acquisition." In R.S.
   Michalski, Jaime G. Carbonell, and Tom M. Mitchell, editors, Machine
   Learning: An Artificial Intelligence Approach, Morgan Kaufmann
   Publishers, San Mateo, CA, 1986.

Theoretical Results:

   Leslie G. Valiant, "A theory of the learnable", Communications
   of the ACM, 27(11):1134--1142, 1984. 

   Haussler, D., "Quantifying Inductive Bias: AI Learning
   Algorithms and Valiant's Learning Framework", Artificial Intelligence,
   36:177-221, 1988.