Machine Learning book cover

Machine Learning

A Multistrategy Approach, Volume IV

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.

Hardbound, 782 Pages

Published: February 1994

Imprint: Morgan Kaufmann

ISBN: 978-1-55860-251-9


  • 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
      Bibliography of Multistrategy Learning Research, by Janusz Wnek and Michael Hieb
      About the Authors
      Author Index
      Subject Index


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