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Machine Learning of Inductive Bias by Paul E. Utgoff (Paperback, 2012)

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Product Information

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.

Product Identifiers

Publisher
Springer-Verlag New York Inc.
ISBN-13
9781461294085
eBay Product ID (ePID)
189390918

Product Key Features

Author
Paul E. Utgoff
Publication Name
Machine Learning of Inductive BIAS
Format
Paperback
Language
English
Subject
Computer Science
Publication Year
2012
Type
Textbook
Number of Pages
166 Pages

Dimensions

Item Height
235mm
Item Width
155mm
Volume
15
Item Weight
296g

Additional Product Features

Title_Author
Paul E. Utgoff
Series Title
The Springer International Series in Engineering and Computer Science
Country/Region of Manufacture
United States

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