Algorithmic Learning Theory

6th International Workshop, Alt '95, Fukuoka, Japan, October 18 - 20, 1995. Proceedings

By: Klaus P. Jantke (Editor), Takeshi Shinohara (Editor), Thomas Zeugmann (Editor)

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Paperback | 5 October 1995

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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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Non-Fiction Computing & I.T.Computer Programming & Software Development Algorithms & Data Structures Computer Science Mathematical Theory of Computation Artificial Intelligence Machine Learning Mathematics

Editors' introduction
Grammatical inference: An old and new paradigm	p. 1
Efficient learning of real time one-counter automata	p. 25
Learning strongly deterministic even linear languages from positive examples	p. 41
Language learning from membership queries and characteristic examples	p. 55
Learning unions of tree patterns using queries	p. 66
Inductive constraint logic	p. 80
Incremental learning of logic programs	p. 95
Learning orthogonal F-Horn formulas	p. 110
Learning nested differences in the presence of malicious noise	p. 123
Learning sparse linear combinations of basis functions over a finite domain	p. 138
Inferring a DNA sequence from erroneous copies	p. 151
Machine induction without revolutionary paradigm shifts	p. 153
Probabilistic language learning under monotonicity constraints	p. 169
Noisy inference and oracles	p. 185
Simulating teams with many conjectures	p. 201
Complexity of network training for classes of neural networks	p. 215
Learning ordered binary decision diagrams	p. 228
Simple PAC learning of simple decision lists	p. 239
The complexity of learning minor closed graph classes	p. 249
Technical and scientific issues of KDD	p. 261
Analogical logic program synthesis algorithm that can refute inappropriate similarities	p. 266
Reflecting and self-confident inductive inference machines	p. 282
On approximately identifying concept classes in the limit	p. 298
Application of Kolmogorov complexity to inductive inference with limited memory	p. 313
Author Index	p. 319
Table of Contents provided by Blackwell. All Rights Reserved.

Algorithmic Learning Theory

6th International Workshop, Alt '95, Fukuoka, Japan, October 18 - 20, 1995. Proceedings

At a Glance

Paperback

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