Machine Learning, 1st Edition

A Multistrategy Approach, Volume IV

Machine Learning, 1st Edition,Ryszard Michalski,George Tecuci,ISBN9781558602519

Michalski   &   Tecuci   

Morgan Kaufmann



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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.

Ryszard Michalski

Affiliations and Expertise

George Mason University

Information about this author is currently not available.

Machine Learning, 1st Edition

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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