Machine Learning of Inductive Bias (Paperback)

Paul E. Utgoff

ISBN 10: 1461294088 / ISBN 13: 9781461294085

Published by Springer-Verlag New York Inc., New York, 2012

About this Item

Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781461294085

Bibliographic Details

• Title: Machine Learning of Inductive Bias (Paperback)
• Publisher: Springer-Verlag New York Inc., New York
• Publication Date: 2012
• Language: English
• ISBN 10: 1461294088
• ISBN 13: 9781461294085
• Binding: Paperback
• Condition: new

About this title

Synopsis

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.