Volume 9, 1992
- Jaime G. Carbonell:
Machine Learning: A Maturing Field.
5-7
- Nicol N. Schraudolph, Richard K. Belew:
Dynamic Parameter Encoding for Genetic Algorithms.
9-21
- Scott Dietzen, Frank Pfenning:
Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization.
23-55
- Michael J. Pazzani, Dennis F. Kibler:
The Utility of Knowledge in Inductive Learning.
57-94
- Vasant Honavar:
Neural Network Design and the Complexity of Learning (Book Review).
95-98
- J. Stephen Judd:
A Reply to Honavar's Book Review of Neural Network Design and the Complexity of Learning.
99-100
- Wolfgang Maass, György Turán:
Lower Bound Methods and Separation Results for On-Line Learning Models.
107-145
- Dana Angluin, Michael Frazier, Leonard Pitt:
Learning Conjunctions of Horn Clauses.
147-164
- Kenji Yamanishi:
A Learning Criterion for Stochastic Rules.
165-203
- Naoki Abe, Manfred K. Warmuth:
On the Computational Complexity of Approximating Distributions by Probabilistic Automata.
205-260
- Daniel N. Osherson, Michael Stob, Scott Weinstein:
A Universal Method of Scientific Inquiry.
261-271
- John R. Anderson, Michael Matessa:
Explorations of an Incremental, Bayesian Algorithm for Categorization.
275-308
- Gregory F. Cooper, Edward Herskovits:
A Bayesian Method for the Induction of Probabilistic Networks from Data.
309-347
- Michael J. Pazzani, Wendy Sarrett:
A Framework for Average Case Analysis of Conjunctive Learning Algorithms.
349-372
- Avrim Blum:
Learning Boolean Functions in an Infinite Attribute Space.
373-386
- Terence C. Fogarty:
First Nearest Neighbor Classification on Frey and Slate's Letter Recognition Problem.
387-388
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