Algorithmic Learning Theory - ALT '92
Third Workshop, ALT '92, Tokyo, Japan, October 20-22, 1992. Proceedings
Published on 20. October 1993
Book
Paperback/Softback
XII, 264 pages
978-3-540-57369-2 (ISBN)
Description
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.
More details
Series
Edition
1993 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XII, 264 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 16 mm
Weight
423 gr
ISBN-13
978-3-540-57369-2 (9783540573692)
DOI
10.1007/3-540-57369-0
Schweitzer Classification
Content
Discovery learning in intelligent tutoring systems.- From inductive inference to algorithmic learning theory.- A stochastic approach to genetic information processing.- On learning systolic languages.- A note on the query complexity of learning DFA.- Polynomial-time MAT learning of multilinear logic programs.- Iterative weighted least squares algorithms for neural networks classifiers.- Domains of attraction in autoassociative memory networks for character pattern recognition.- Regularization learning of neural networks for generalization.- Competitive learning by entropy minimization.- Inductive inference with bounded mind changes.- Efficient inductive inference of primitive Prologs from positive data.- Monotonic language learning.- Prudence in vacillatory language identification (Extended abstract).- Implementation of heuristic problem solving process including analogical reasoning.- Planning with abstraction based on partial predicate mappings.- Learning k-term monotone Boolean formulae.- Some improved sample complexity bounds in the probabilistic PAC learning model.- An application of Bernstein polynomials in PAC model.- On PAC learnability of functional dependencies.- Protein secondary structure prediction based on stochastic-rule learning.- Notes on the PAC learning of geometric concepts with additional information.