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ORCIDMachine Learning, Volume 9
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 G. 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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