
Computational Learning Theory and Natural Learning Systems: Volume 3
Selecting Good Models
MIT Press
Published on 27. April 1995
Book
Paperback/Softback
405 pages
978-0-262-66096-9 (ISBN)
Description
This is the third in a series of edited volumes exploring the evolving landscape of learning systems research which spans theory and experiment, symbols and signals. It continues the exploration of the synthesis of the machine learning subdisciplines begun in volumes I and II. The nineteen contributions cover learning theory, empirical comparisons of learning algorithms, the use of prior knowledge, probabilistic concepts, and the effect of variations over time in the concepts and feedback from the environment.The goal of this series is to explore the intersection of three historically distinct areas of learning research: computational learning theory, neural networks andAI machine learning. Although each field has its own conferences, journals, language, research, results, and directions, there is a growing intersection and effort to bring these fields into closer coordination.Can the various communities learn anything from one another? These volumes present research that should be of interest to practitioners of the various subdisciplines of machine learning, addressing questions that are of interest across the range of machine learning approaches, comparing various approaches on specific problems and expanding the theory to cover more realistic cases.A Bradford Book
More details
Language
English
Place of publication
Cambridge, Mass.
United States
Publishing group
MIT Press Ltd
Target group
College/higher education
Professional and scholarly
Product notice
Paperback (trade)
Dimensions
Height: 229 mm
Width: 178 mm
Thickness: 25 mm
Weight
454 gr
ISBN-13
978-0-262-66096-9 (9780262660969)
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Schweitzer Classification
Persons
Author
Avaya Inc
Rutgers University
University of Wisconsin