Machine Learning of Inductive Bias (Paperback, Softcover reprint of the original 1st ed. 1986)

This book is based on the author's Ph.D. dissertation 56]. The the sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias."

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	The Springer International Series in Engineering and Computer Science, 15
Release date:	April 2012
First published:	1986
Authors:	Paul E Utgoff
Dimensions:	235 x 155 x 10mm (L x W x T)
Format:	Paperback
Pages:	166
Edition:	Softcover reprint of the original 1st ed. 1986
ISBN-13:	978-1-4612-9408-5
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	1-4612-9408-8
Barcode:	9781461294085

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