Algorithmic Learning Theory: Third Workshop, ALT ’92 Tokyo, Japan, October 20–22, 1992 Proceedings

Setsuko Otsuki (auth.), Shuji Doshita, Koichi Furukawa, Klaus P. Jantke, Toyaki Nishida (eds.)3540573690, 9783540573692

This volume contains the papers that were presented at the Third Workshop onAlgorithmic Learning Theory, held in Tokyo in October 1992. In addition to 3invited papers, the volume contains 19 papers accepted for presentation, selected from 29 submitted extended abstracts. The ALT workshops have been held annually since 1990 and are organized and sponsored by the Japanese Society for Artificial Intelligence. The main objective of these workshops is to provide an open forum for discussions and exchanges of ideasbetween researchers from various backgrounds in this emerging, interdisciplinary field of learning theory. The volume is organized into parts on learning via query, neural networks, inductive inference, analogical reasoning, and approximate learning.

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Table of contents :
Discovery learning in intelligent tutoring systems….Pages 1-12
From inductive inference to algorithmic learning theory….Pages 13-24
A stochastic approach to genetic information processing….Pages 25-36
On learning systolic languages….Pages 37-52
A note on the query complexity of learning DFA….Pages 53-62
Polynomial-time MAT learning of multilinear logic programs….Pages 63-74
Iterative weighted least squares algorithms for neural networks classifiers….Pages 75-86
Domains of attraction in autoassociative memory networks for character pattern recognition….Pages 87-98
Regularization learning of neural networks for generalization….Pages 99-110
Competitive learning by entropy minimization….Pages 111-122
Inductive inference with bounded mind changes….Pages 123-134
Efficient inductive inference of primitive Prologs from positive data….Pages 135-146
Monotonic language learning….Pages 147-158
Prudence in vacillatory language identification (Extended abstract)….Pages 159-168
Implementation of heuristic problem solving process including analogical reasoning….Pages 169-182
Planning with abstraction based on partial predicate mappings….Pages 183-194
Learning k -term monotone Boolean formulae….Pages 195-207
Some improved sample complexity bounds in the probabilistic PAC learning model….Pages 208-219
An application of Bernstein polynomials in PAC model….Pages 220-228
On PAC learnability of functional dependencies….Pages 229-239
Protein secondary structure prediction based on stochastic-rule learning….Pages 240-251
Notes on the PAC learning of geometric concepts with additional information….Pages 252-259