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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.
Machine Learning: A Multistrategy Approach, Volume IV
Edited by Ryszard Michalski and Gheorghe Tecuci
- Preface, by Ryszard S. Michalski and Gheorghe Tecuci
Part One General Issues
- Chapter 1 Inferential Theory of Learning: Developing Foundations for Multistrategy Learning, by Ryszard S. Michalski
Chapter 2 The Fiction and Nonfiction of Features, by Edward J. Wisniewski and Douglas L. Medin
Chapter 3 Induction and the Organization of Knowledge, by Yves Kodratoff
Chapter 4 An Inference-Based Framework for Multistrategy Learning, by Gheorghe Tecuci
Part Two Theory Revision
- Chapter 5 A Multistrategy Approach to Refinement, by Raymond J. Mooney and Dirk Ourston
Chapter 6 Theory Completion Using Knowledge-based Learning, by Bradley L. Whitehall and Stephen C-Y. Lu
Chapter 7 GEMINI: An Integration of Analytical and Emirical Learning, by Andrea P. Danyluk
Chapter 8 Theory Revision by Analyzing Explanations and Prototypes, by Stan Matwina dn Boris Plante
Chapter 9 Interactive Theory Revision, by Luc De Raedt and Maurice Bruynooghe
Part Three Cooperative Integration
- Chapter 10 Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning, by Michael Pazzani
Chapter 11 Balanced Cooperative Modeling, by Katharina Morik
Chapter 12 WHY: A System That Learns Using Causal Models and Examples, by Cristina Baroglio, Marco Botta, and Lorenza Saitta
Chapter 13 Introspective Reasoning Using Meta-Explanations for Multistrategy Learning, by Ashwin Ram and Michael Cox
Chapter 14 Macro and Micro Perspectives of Multistrategy Learning, by Yoram Reich
Part Four Symbolic and Subsymbolic Learning
- Chapter 15 Refining Symbolic Knowledge Using Neural Networks, by Geoffrey G. Towell and Jude W. Shavlik
Chapter 16 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Strategies, by Jianping Zhang
Chapter 17 Improving a Rule Induction System Using Genetic Algorithms, by Haleh Vafaie and Kenneth De Jong
Chapter 18 Multistrategy Learning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms, by Jerzy W. Bala, Kenneth A. De Jong, and Peter W Pachowicz
Chapter 19 Comparing Symbolic and Subsymbolic Learning: Three Studies, by Janusz Wnek and Ryszard S. Michalski
Part Five Special Topics and Applications
- Chapter 20 Case-Based Reasoning in PRODIGY, by Manuela Veloso and Jaime Carbonell
Chapter 21 Genetic Programming: Evolutionary Approaches to Multistrategy Learning, by Hugo de Garis
Chapter 22 Experience-based Adaptive Search, by Jeffrey Gould and Robert Levinson
Chapter 23 Classifying for Prediction: A Multistrategy Approach to Predicting Protein Structure, by Lawrence Hunter
Chapter 24 GEST: A Learning Computer Vision System That Recognizes Hand Gestures, by Jakub Segen
Chapter 25 Learning with a Qualitative Domain Theory by Means of Plausible Explanations, by Gerhard Widmer
About the Authors
- No. of pages:
- © Morgan Kaufmann 1993
- 8th March 1994
- Morgan Kaufmann
- Hardcover ISBN:
- Paperback ISBN:
- eBook ISBN:
George Mason University
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