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 BibTeX
- Hans Tallis:
Tuning Rule-Based Systems to Their Environments.
8-14 BibTeX
- Brent J. Krawchuk, Ian H. Witten:
On Asking the Right Questions.
15-21 BibTeX
- Douglas H. Fisher, Jeffrey C. Schlimmer:
Concept Simplification and Prediction Accuracy.
22-28 BibTeX
- Jakub Segen:
Learning Graph Models of Shape.
29-35 BibTeX
- Kent A. Spackman:
Learning Categorical Decision Criteria in Biomedical Domains.
36-46 BibTeX
- Jakub Segen:
Conceptual Clumping of Binary Vectors with Occam's Razor.
47-53 BibTeX
- Peter Cheeseman, James Kelly, Matthew Self, John Stutz, Will Taylor, Don Freeman:
AutoClass: A Bayesian Classification System.
54-64 BibTeX
- Klaus P. Gross:
Incremental Multiple Concept Learning Using Experiments.
65-72 BibTeX
- Wayne Iba, James Wogulis, Pat Langley:
Trading Off Simplicity and Coverage in Incremental concept Learning.
73-79 BibTeX
- Michael Lebowitz:
Deferred Commitment in UNIMEM: Waiting to Learn.
80-86 BibTeX
- Jarryl Wirth, Jason Catlett:
Experiments on the Costs and Benefits of Windowing in ID3.
87-99 BibTeX
- Jie Cheng, Usama M. Fayyad, Keki B. Irani, Zhaogang Qian:
Improved Decision Trees: A Generalized Version of ID3.
100-106 BibTeX
- Paul E. Utgoff:
ID5: An Incremental ID3.
107-120 BibTeX
- Ming Tan, Larry J. Eshelman:
Using Weighted Networks to Represent Classification Knowledge in Noisy Domains.
121-134 BibTeX
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
Copyright © Sat May 16 23:20:38 2009
by Michael Ley (ley@uni-trier.de)
