Machine Learning: An Artificial Intelligence Approach, Volume 4

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Morgan Kaufmann, 1994 - Computers - 782 pages
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Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area.

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Contents

The Fiction and Nonfiction of Features
63
Induction and the Organization of Knowledge
85
An InferenceBased Framework
107
A Multistrategy Approach to Theory Refinement
141
Theory Completion Using Knowledgebased Learning
165
An Integration of Analytical
189
Theory Revision by Analyzing Explanations
217
Interactive Theory Revision
239
Improving a Rule Induction System
453
Multistrategy Learning from Engineering Data
471
Comparing Symbolic
489
PART FIVE SPECIAL TOPICS AND APPLICATIONS
521
Evolutionary Approaches
549
Experiencebased Adaptive Search
579
A Multistrategy Approach
605
A Learning Computer Vision System
621

PART THREE COOPERATIVE INTEGRATION
265
Balanced Cooperative Modeling
295
A System That Learns Using Causal Models
319
Introspective Reasoning Using MetaExplanations
349
Macro and Micro Perspectives
379
PART FOUR SYMBOLIC AND SUBSYMBOLIC LEARNING
403
Learning Graded Concept Descriptions
431
Learning with a Qualitative Domain Theory
635
Bibliography of Multistrategy Learning Research
657
About the Authors
731
Author Index
743
Subject Index
757
Copyright

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