[comp.edu] Information about new book on machine learning

morgan@unix.SRI.COM (Morgan Kaufmann) (10/26/90)

Morgan Kaufmann announces a new publication in its Series in
Machine Learning (Edited by Pat Langley)


                  READINGS IN MACHINE LEARNING


          EDITED BY JUDE SHAVLIK (Univ. of Wisconsin) 
            and THOMAS DIETTERICH (Oregon State Univ)


The ability to learn is a fundamental characteristic of intelligent
behavior. Consequently, machine learning has been a focus of
artificial intelligence since the beginnings of AI in the 1950's. 
The past decade has seen tremendous growth in the field, and this
growth promises to continue with valuable contributions to science,
engineering, and business.  

"Readings in Machine Learning" collects the best of the published
machine learning literature including papers that address a wide
range of learning tasks and that introduce a variety of techniques
for giving machines the ability to learn.  It contains papers on
symbolic inductive learning, connectionism, genetic algorithms,
explanation-based learning, discovery systems, and analogical and
case-based learning.  The editors, in conjunction with a group of
expert referees, have chosen important papers that empirically
study, theoretically analyze, or psychologically justify machine
learning algorithms.  Recent papers as well as seminal reports on
early research are included.  The papers are grouped into a dozen
categories, each of which is introduced by the editors.  

ISBN 1-55860-143-0
853 pages, softbound
Price: $39.95 (ordering information follows table of contents)

                        TABLE OF CONTENTS

General Aspects of Machine Learning
     INTRODUCTION 
     Learning at the Knowledge Level
          T. G. Dietterich
     Problem Solving and Rule Induction: A Unified View
          H. A. Simon and G. Lea
     Machine Learning as an Experimental Science
          D. Kibler and P. Langley

Inductive Learning From Preclassified Training Examples
     INTRODUCTION 
     ALGORITHMS 
     Induction of Decision Trees
          J. R. Quinlan
     A Theory and Methodology of Inductive Learning
          R. S. Michalski
     Generalization as Search
          T. M. Mitchell
     Learning Representative Exemplars of Concepts: An Initial Case
          Study
          D. Kibler and D. W. Aha
     Learning Internal Representations by Error Propagation
          D. E. Rumelhart, G. E. Hinton, and R. J. Williams
     The Perceptron: A Probabilistic Model for Information Storage
          and Organization in the Brain
          F. Rosenblatt
     A Time-Delay Neural Network Architecture for Isolated Word
          Recognition
          K. J. Lang, A. H. Waibel, and G. E. Hinton
     EMPIRICAL COMPARISON
     An Experimental Comparison of Symbolic and Connectionist
          Learning Algorithms
          R. Mooney, J. Shavlik, G. Towell, and A. Grove
     An Empirical Comparison of Pattern Recognition, Neural Nets,
          and Machine Learning Classification Methods
          S. M. Weiss and I. Kapouleas
     THEORY
     The Need for Biases in Learning Generalizations
          T. M. Mitchell
     A Theory of the Learnable
          L. G. Valiant
     Occam's Razor
          A. Blumer, A. Ehrenfeucht, D. Haussler, and
          M. K. Warmuth
     Quantifying Inductive Bias: AI Learning Algorithms and
          Valiant's Learning Framework
          D. Haussler
     Learning
          M. Minsky and S. A. Papert
     On the Complexity of Loading Shallow Neural Networks
          S. Judd
     What Size Net Gives Valid Generalization?
          E. B. Baum and D. Haussler

Unsupervised Concept Learning and Discovery
     INTRODUCTION 
     CLUSTERING
     Knowledge Acquisition Via Incremental Conceptual Clustering
          D. H. Fisher
     The Simulation of Verbal Learning Behavior
          E. A. Feigenbaum"
     AutoClass: A Bayesian Classification System
          P. Cheeseman, J. Kelly, M. Self, J. Stutz, W. Taylor, and
          D. Freeman
     Feature Discovery by Competitive Learning
          D. E. Rumelhart and D. Zipser
     Self-Organized Formation of Topologically Correct Feature Maps
          T. Kohonen
     DISCOVERY
     The Ubiquity of Discovery
          D. B. Lenat
     Heuristics for Empirical Discovery
          P. Langley, H. A. Simon, and G. L. Bradshaw
     A Unified Approach to Explanation and Theory Formation
          B. Falkenhainer
     Classifier Systems and Genetic Algorithms
          L. B. Booker, D. E. Goldberg, and J. H. Holland

Improving the Efficiency of a Problem Solver
     INTRODUCTION
     LEARNING COMPOSITE RULES
     Explanation-Based Generalization: A Unifying View
          T. M. Mitchell, R. M. Keller, and S. T. Kedar-Cabelli
     Explanation-Based Learning: An Alternative View
          G. DeJong and R. Mooney
     Learning and Executing Generalized Robot Plans
          R. E. Fikes, P. E. Hart, and N. J. Nilsson
     Acquiring Recursive and Iterative Concepts with
          Explanation-Based Learning
          J. W. Shavlik
     LEARNING SEARCH CONTROL KNOWLEDGE
     Learning by Experimentation: Acquiring and Refining
          Problem-Solving Heuristics
          T. M. Mitchell, P. E. Utgoff, and R. Banerji
     Credit Assignment in Rule Discovery Systems Based on Genetic
          Algorithms
          J. J. Grefenstette
     Some Studies in Machine Learning Using the Game of Checkers
          A. L. Samuel
     Chunking in Soar: The Anatomy of a General Learning Mechanism
          J. E. Laird, P. S. Rosenbloom, and A. Newell
     Quantitative Results Concerning the Utility of
          Explanation-Based Learning
          S. Minton
     Defining Operationality for Explanation-Based Learning
          R. M. Keller

Using Preexisting Domain Knowledge Inductively
     INTRODUCTION
     ANALOGICAL APPROACHES
     The Mechanisms of Analogical Learning
          D. Gentner
     Combining Analogies in Mental Models
          M. H. Burstein
     Derivational Analogy: A Theory of Reconstructive Problem
     Solving and Expertise Acquisition
          J. G. Carbonell
     Toward a Computational Model of Purpose-Directed Analogy
          S. Kedar-Cabelli
     A Logical Approach to Reasoning by Analogy
          T. R. Davies and S. J. Russell
     A Theory of the Origins of Human Knowledge
          J. R. Anderson
     CASE-BASED APPROACHES
     CHEF
          K. J. Hammond
     Concept Learning and Heuristic Classification in Weak-Theory
          Domains
          B. W. Porter, R. Bareiss, and R. C. Holte
     EXPLANATORY/INDUCTIVE HYBRIDS
     Learning One Subprocedure per Lesson
          K. VanLehn
     Induction of Augmented Transition Networks
          J. R. Anderson
     Learning by Failing to Explain: Using Partial Explanation to
          Learn in Incomplete and Intractable Domains
          R. J. Hall
     A Study of Explanation-Based Methods for Inductive Learning
          N. S. Flann and T. G. Dietterich
     An Approach to Combining Explanation-Based and Neural Learning
          Algorithms
          J. W. Shavlik and G. G. Towell
Index 

Credits

_________________________________________________________________

Ordering Information:

     Please add $3.50 for the first book and $2.50 for each
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