Overview
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 997)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
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Table of contents (24 papers)
Keywords
About this book
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.
Bibliographic Information
Book Title: Algorithmic Learning Theory
Book Subtitle: 6th International Workshop, ALT '95, Fukuoka, Japan, October 18 - 20, 1995. Proceedings
Editors: Klaus P. Jantke, Takeshi Shinohara, Thomas Zeugmann
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/3-540-60454-5
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 1995
Softcover ISBN: 978-3-540-60454-9Published: 05 October 1995
eBook ISBN: 978-3-540-47470-8Published: 13 July 2005
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XV, 324