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 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. California residents please add sales tax appropriate to your county. Morgan Kaufmann Publishers Department 59 2929 Campus Drive, Suite 260 San Mateo, CA 94403 USA Phone: (800)745-READ, (415) 578-9928 Fax: (415) 578-0672 Email: morgan@unix.sri.com