Algorithmic Learning Theory: 6th International Workshop,ALT ’95 Fukuoka, Japan, October 18–20, 1995 Proceedings

Yasubumi Sakakibara (auth.), Klaus P. Jantke, Takeshi Shinohara, Thomas Zeugmann (eds.)3540604545, 9783540604549

This book constitutes the refereed proceedings of the 6th International Workshop on Algorithmic Learning Theory, ALT ’95, held in Fukuoka, Japan, in October 1995.
The book contains 21 revised full papers selected from 46 submissions together with three invited contributions. It covers all current areas related to algorithmic learning theory, in particular the theory of machine learning, design and analysis of learning algorithms, computational logic aspects, inductive inference, learning via queries, artificial and biologicial neural network learning, pattern recognition, learning by analogy, statistical learning, inductive logic programming, robot learning, and gene analysis.

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Table of contents :
Grammatical inference: An old and new paradigm….Pages 1-24
Efficient learning of real time one-counter automata….Pages 25-40
Learning strongly deterministic even linear languages from positive examples….Pages 41-54
Language learning from membership queries and characteristic examples….Pages 55-65
Learning unions of tree patterns using queries….Pages 66-79
Inductive constraint logic….Pages 80-94
Incremental learning of logic programs….Pages 95-109
Learning orthogonal F -Horn formulas….Pages 110-122
Learning nested differences in the presence of malicious noise….Pages 123-137
Learning sparse linear combinations of basis functions over a finite domain….Pages 138-150
Inferring a DNA sequence from erroneous copies (abstract)….Pages 151-152
Machine induction without revolutionary paradigm shifts….Pages 153-168
Probabilistic language learning under monotonicity constraints….Pages 169-184
Noisy inference and oracles….Pages 185-200
Simulating teams with many conjectures….Pages 201-214
Complexity of network training for classes of Neural Networks….Pages 215-227
Learning ordered binary decision diagrams….Pages 228-238
Simple PAC learning of simple decision lists….Pages 239-248
The complexity of learning minor closed graph classes….Pages 249-260
Technical and scientific issues of KDD (or: Is KDD a science?)….Pages 261-265
Analogical logic program synthesis algorithm that can refute inappropriate similarities….Pages 266-281
Reflecting and self-confident inductive inference machines….Pages 282-297
On approximately identifying concept classes in the limit….Pages 298-312
Application of kolmogorov complexity to inductive inference with limited memory….Pages 313-318