morgan@unix.SRI.COM (Morgan Kaufmann) (10/15/90)
Morgan Kaufmann announces the publication of the third volume of
the publication Machine Learning:
MACHINE LEARNING:
AN ARTIFICIAL INTELLIGENCE APPROACH
VOLUME III
edited by
YVES KODRATOFF
(French National Scientific Research Council)
and
RYSZARD MICHALSKI
(George Mason University)
This volume presents a comprehensive review of research in machine
learning. Having experienced tremendous growth in recent years,
this area is now one of the most central and active research areas
of artificial intelligence. MACHINE LEARNING, VOLUME III reflects
this recent progress by presenting work from the major subareas of
the field including empirical learning methods, analytical methods,
integrated learning systems, neural networks, genetic algorithms
and formal approaches. Most chapters are invited original
contributions by leading researchers in the field. The book also
contains an exhaustive bibliography encompassing the period 1986
through 1989 and containing over 1000 entries.
This book is the third in a series of important collections in this
area which includes Volume I (1983) and Volume II (1986). Each
volume may be used independently. Both previous volumes have been
extremely well received and are widely used as textbooks and
reference works. Volume III, in addition to presenting new topics
of research not covered in the earlier volumes, also covers a
broader international scope reflecting the expansion of the field
throughout the world.
ISBN: 1-55860-119-8
825 pages, hardcover
Price: $49.95
Table of Contents
PART ONE -- GENERAL ISSUES
Chapter 1 Research in Machine Learning; Recent Progress,
Classification of Methods, and Future Directions
Ryszard S. Michalski and Yves Kodratoff
Chapter 2 Explanations, Machine Learning, and Creativity
Roger Schank and Alex Kass, Commentary by Jean-
Gabriel Ganascia
PART TWO -- EMPIRICAL LEARNING METHODS
Chapter 3 Learning Flexible Concepts: Fundamental Ideas and
a Method Bases on Two-Tiered Representation
Ryszard S. Michalski, Commentary by Robert E.
Stepp
Chapter 4 Protos: an Exemplar-Based Learning Apprentice
E. Ray Bareiss, Bruce W. Porter, and Craig C.
Wier, Commentary by Robert C. Holte
Chapter 5 Probabilistic Decision Trees
J.R. Quinlan
Chapter 6 Integrating Quantitative and Qualitative Discovery
in the ABACUS System
Brian C. Falkenhainer and Ryszard S. Michalski
Chapter 7 Learning by Experimentation: The Operator
Refinement Method
Jaime G. Carbonell and Yolanda Gil
Chapter 8 Learning Fault Diagnosis Heuristics from Device
Descriptions
Michael J. Pazzani
Chapter 9 Conceptual Clustering and Categorization: Bridging
the Gap between Induction and Causal Models
Stephen Jose Hanson
PART THREE -- ANALYTICAL LEARNING METHODS
Chapter 10 LEAP: A Learning Apprentice System for VLSI Design
Tom M. Mitchell, Sridhar Mahadevan, and Louis
I. Steinberg, Commentary by Pavel B. Brazdil
Chapter 11 Acquiring General Iterative Concepts by
Reformulating Explanations of Observed Examples
Jude W. Shavlik and Gerald F. DeJong
Chapter 12 Discovering Algorithms from Weak Methods
Armand E. Prieditis
Chapter 13 OGUST: A System that Learns Using Domain Properties
Expressed as Theorems
Christel Vrain
Chapter 14 Conditional Operationality and Explanation-based
Generalization
Haym Hirsh
PART FOUR -- INTEGRATED LEARNING SYSTEMS
Chapter 15 The Utility of Similarity-based Learning in a World
Needing Explanation
Michael Lebowitz, Commentary by Larry A.
Rendell
Chapter 16 Learning Expert Knowledge by Improving the
Explanations Provided by the System
Yves Kodratoff, Commentary by Robert E. Stepp
Chapter 17 Guiding Induction with Domain Theories
Francesco Bergadano and Attilio Giordana
Chapter 18 Knowledge Base Refinement as Improving an Incorrect
and Incomplete Domain Theory
David C. Wilkins
Chapter 19 Apprenticeship Learning in Imperfect Domain Theories
Gheorge Tecuci and Yves Kodratoff
PART FIVE -- SUBSYMBOLIC AND HETEROGENOUS LEARNING SYSTEMS
Chapter 20 Connectionist Learning Procedures
Geoffrey I. Hinton
Chapter 21 Genetic-Algorithm-based Learning
Kenneth DeJong
PART SIX -- FORMAL ANALYSIS
Chapter 22 Applying Valiant's Learning Framework to AI Concept-
Learning Problems
David Haussler
Chapter 23 A New Approach to Unsupervised Learning in
Deterministic Environments
Ronald L. Rivest and Robert E. Schapire
Bibliography of Recent Machine Learning Research (1985-1989)
Pawel A. Stefanski, Janusz Wnek, and Jianping Zhang
About the Authors
Author Index
Subject Index
_________________________________________________________________
Ordering Information:
Please add $3.50 for the first book and $2.50 for each
additional for surface shipping to the U.S. and Canada; $6.50
for the first book and $3.50 for each additional for shipping
to all other areas.
Master Card, Visa and personal checks drawn on US banks
accepted.
Morgan Kaufmann Publishers
Department 55
2929 Campus Drive, Suite 260
San Mateo, CA 94403
USA
Phone: (415) 578-9928
Fax: (415) 578-0672
email: morgan@unix.sri.com