Name: Machine Learning of Inductive Bias
SKU: 58132053
Price: 3431.00 TWD
Availability: InStock
Author: Utgoff, Paul E.
ISBN: 9781461294085

portada Machine Learning of Inductive Bias

Type

Physical Book

Author

Paul E. Utgoff

Publisher

Springer

Language

English

Pages

166

Format

Paperback

Dimensions

23.4x15.6x1 cm

Weight

0.27 kg.

ISBN13

9781461294085

Synopsis "Machine Learning of Inductive Bias"

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.

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Machine Learning of Inductive Bias - Utgoff, Paul E.

Synopsis "Machine Learning of Inductive Bias"

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