Machine Learning of Inductive Bias
Published on 5. April 2012
Book
Paperback/Softback
XVIII, 166 pages
978-1-4612-9408-5 (ISBN)
Description
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.
More details
Series
Edition
Softcover reprint of the original 1st ed. 1986
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
XVIII, 166 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 11 mm
Weight
295 gr
ISBN-13
978-1-4612-9408-5 (9781461294085)
DOI
10.1007/978-1-4613-2283-2
Schweitzer Classification
Other editions
Additional editions
Paul E. Utgoff
Machine Learning of Inductive Bias
Book
06/1986
Kluwer Academic Publishers
€106.99
Shipment within 3-4 weeks
Content
1 Introduction.- 1.1 Machine Learning.- 1.2 Learning Concepts from Examples.- 1.3 Role of Bias in Concept Learning.- 1.4 Kinds of Bias.- 1.5 Origin of Bias.- 1.6 Learning to Learn.- 1.7 The New-Term Problem.- 1.8 Guide to Remaining Chapters.- 2 Related Work.- 2.1 Learning Programs that use a Static Bias.- 2.2 Learning Programs that use a Dynamic Bias.- 3 Searching for a Better Bias.- 3.1 Simplifications.- 3.2 The RTA Method for Shifting Bias.- 4 LEX and STABB.- 4.1 LEX: A Program that Learns from Experimentation.- 4.2 STABB: a Program that Shifts Bias.- 5 Least Disjunction.- 5.1 Procedure.- 5.2 Requirements.- 5.3 Experiments.- 5.4 Example Trace.- 5.5 Discussion.- 6 Constraint Back-Propagation.- 6.1 Procedure.- 6.2 Requirements.- 6.3 Experiments.- 6.4 Example Trace.- 6.5 Discussion.- 7 Conclusion.- 7.1 Summary.- 7.2 Results.- 7.3 Issues.- 7.4 Further Work.- Appendix A: Lisp Code.- A.1 STABB.- A.2 Grammar.- A.3 Intersection.- A.4 Match.- A.5 Operators.- A.6 Utilities.