5. ML 1988:
Ann Arbor,
Michigan,
USA
John E. Laird (Ed.):
Machine Learning, Proceedings of the Fifth International Conference on Machine Learning, Ann Arbor, Michigan, USA, June 12-14, 1988.
Morgan Kaufmann 1988, ISBN 0-934613-64-8 
Empirical Learning
- Randy Kerber:
Using a Generalization Hierarchy to Learn from Examples.
1-7
- Hans Tallis:
Tuning Rule-Based Systems to Their Environments.
8-14
- Brent J. Krawchuk, Ian H. Witten:
On Asking the Right Questions.
15-21
- Douglas H. Fisher, Jeffrey C. Schlimmer:
Concept Simplification and Prediction Accuracy.
22-28
- Jakub Segen:
Learning Graph Models of Shape.
29-35
- Kent A. Spackman:
Learning Categorical Decision Criteria in Biomedical Domains.
36-46
- Jakub Segen:
Conceptual Clumping of Binary Vectors with Occam's Razor.
47-53
- Peter Cheeseman, James Kelly, Matthew Self, John Stutz, Will Taylor, Don Freeman:
AutoClass: A Bayesian Classification System.
54-64
- Klaus P. Gross:
Incremental Multiple Concept Learning Using Experiments.
65-72
- Wayne Iba, James Wogulis, Pat Langley:
Trading Off Simplicity and Coverage in Incremental concept Learning.
73-79
- Michael Lebowitz:
Deferred Commitment in UNIMEM: Waiting to Learn.
80-86
- Jarryl Wirth, Jason Catlett:
Experiments on the Costs and Benefits of Windowing in ID3.
87-99
- Jie Cheng, Usama M. Fayyad, Keki B. Irani, Zhaogang Qian:
Improved Decision Trees: A Generalized Version of ID3.
100-106
- Paul E. Utgoff:
ID5: An Incremental ID3.
107-120
- Ming Tan, Larry J. Eshelman:
Using Weighted Networks to Represent Classification Knowledge in Noisy Domains.
121-134
Genetic Learning
Connectionist Learning
Explanation-Based Learning
Integrated Explanation-Based and Empirical Learning
Case-Based Learning
Machine Discovery
Formal Models of Concept Learning
Experimental Results in Machine Learning
Computational Impact of Learning and Forgetting
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