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.

Keywords

Authors and Affiliations

University of Massachusetts, Amherst, USA

Paul E. Utgoff

Bibliographic Information

Book Title: Machine Learning of Inductive Bias
Authors: Paul E. Utgoff
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4613-2283-2
Publisher: Springer New York, NY
eBook Packages: Springer Book Archive
Copyright Information: Kluwer Academic Publishers 1986
Hardcover ISBN: 978-0-89838-223-5Published: 30 June 1986
Softcover ISBN: 978-1-4612-9408-5Published: 05 April 2012
eBook ISBN: 978-1-4613-2283-2Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XVIII, 166
Topics: Artificial Intelligence

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Table of contents (7 chapters)

Front Matter

Introduction

Related Work

Searching for a Better Bias

LEX and STABB

Least Disjunction

Constraint Back-Propagation

Conclusion

Back Matter

About this book