Machine Learning Proceedings 1988. Proceedings of the Fifth International Conference on Machine Learning, June 12–14, 1988, University of Michigan, Ann Arbor

Proceedings of June 1988. Original articles focus on many areas of machine learning including empirical methods, explanation-based methods, genetic algorithms, connectionist learning, probabilistic methods and formal theories of learning. No index. Annotation copyright Book News, Inc. Portland, Or Content: Front Matter, Page i Copyright, Page ii PREFACE, Page vii, John Laird Using a Generalization Hierarchy to Learn from Examples, Pages 1-7, RANDY G. KERBER Tuning Rule-Based Systems to Their Environments, Pages 8-14, HANS TALLIS ON ASKING THE RIGHT QUESTIONS, Pages 15-21, BRENT J. KRAWCHUK, IAN H. WITTEN Concept Simplification and Prediction Accuracy, Pages 22-28, DOUGLAS H. FISHER, JEFFREY C. SCHLIMMER Learning Graph Models of Shape, Pages 29-35, JAKUB SEGEN Learning Categorical Decision Criteria in Biomedical Domains, Pages 36-46, KENT A. SPACKMAN Conceptual Clumping of Binary Vectors with Occam's Razor, Pages 47-53, JAKUB SEGEN AutoClass: A Bayesian Classification System, Pages 54-64, PETER CHEESEMAN, JAMES KELLY, MATTHEW SELF, JOHN STUTZ, WILL TAYLOR, DON FREEMAN Incremental Multiple Concept Learning Using Experiments, Pages 65-72, KLAUS P. GROSS Trading Off Simplicity and Coverage in Incremental Concept Learning, Pages 73-79, WAYNE IBA, JAMES WOGULIS, PAT LANGLEY Deferred Commitment in UNIMEM: Waiting to Learn, Pages 80-86, MICHAEL LEBOWITZ Experiments on the Costs and Benefits of Windowing in ID3, Pages 87-99, JARRYL WIRTH, JASON CATLETT Improved Decision Trees: A Generalized Version of ID3, Pages 100-106, Jie Cheng, Usama M. Fayyad, Keki B. Irani, Zhaogang Qian ID5: An Incremental ID3, Pages 107-120, PAUL E. UTGOFF Using Weighted Networks to Represent Classification Knowledge in Noisy Domains, Pages 121-134, MING TAN, LARRY ESHELMAN An Empirical Comparison of Genetic and Decision-Tree Classifiers, Pages 135-141, J.R. QUINLAN Population Size In Classifier Systems, Pages 142-152, GEORGE G. ROBERTSON Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms, Pages 153-161, Richard A. Caruana, J. David Schaffer Classifier Systems with Hamming Weights, Pages 162-173, Lawrence Davis, David K. Young Midgard: A Genetic Approach to Adaptive Load Balancing for Distributed Systems, Pages 174-180, ADRIAN V. SANNIER II, ERIKD GOODMAN Some Interesting Properties of a Connectionist Inductive Learning System, Pages 181-187, Edward J. Wisniewski, James A. Anderson Competitive Reinforcement Learning, Pages 188-199, KENTON J. LYNNE Connectionist Learning of Expert Backgammon Evaluations, Pages 200-206, G. Tesauro Building and Using Mental Models in a Sensory-Motor Domain: A Connectionist Approach, Pages 207-213, Bartlett W. Mel Reasoning about Operationality for Explanation-Based Learning, Pages 214-220, HAYM HIRSH Boundaries of Operationality, Pages 221-234, MICHAEL S. BRAVERMAN, STUART J. RUSSELL On the Tractability of Learning from Incomplete Theories, Pages 235-241, SRIDHAR MAHADEVAN, PRASAD TADEPALLI ACTIVE EXPLANATION REDUCTION: An Approach to the Multiple Explanations Problem, Pages 242-255, SHANKAR A. RAJAMONEY, GERALD F. DEJONG Generalizing Number and Learning from Multiple Examples in Explanation Based Learning, Pages 256-269, WILLIAM W. COHEN Generalizing the Order of Operators in Macro-Operators, Pages 270-283, RAYMOND J. MOONEY Using Experience-Based Learning in Game Playing, Pages 284-290, Kenneth A. De Jong, Alan C. Schultz Integrated Learning with Incorrect and Incomplete Theories, Pages 291-297, Michael J. Pazzani An Approach Based on Integrated Learning to Generating Stories from Stories, Pages 298-304, CLAUDIO CARPINETO A KNOWLEDGE INTENSIVE APPROACH TO CONCEPT INDUCTION, Pages 305-317, FRANCESCO BERGADANO, ATTILIO GIORDANA Learning to Program by Examining and Modifying Cases, Pages 318-324, Robert S. Williams Theory Discovery and the Hypothesis Language, Pages 325-338, Kevin T. Kelly Machine Invention of First-order Predicates by Inverting Resolution, Pages 339-352, STEPHEN MUGGLETON, WRAY BUNTINE The Interdependencies of Theory Formation, Revision, and Experimentation, Pages 353-366, BRIAN FALKENHAINER, SHANKAR RAJAMONEY A Hill-Climbing Approach to Machine Discovery, Pages 367-373, DONALD ROSE, PAT LANGLEY REDUCTION: A PRACTICAL MECHANISM OF SEARCHING FOR REGULARITY IN DATA, Pages 374-380, Yi-Hua Wu Extending the Valiant Learning Model, Pages 381-394, JONATHAN AMSTERDAM LEARNING SYSTEMS OF FIRST-ORDER RULES, Pages 395-401, NICOLAS HELFT Two New Frameworks for Learning, Pages 402-415, B.K. Natarajan, P. Tadepalli Hypothesis Filtering: A Practical Approach to Reliable Learning, Pages 416-429, OREN ETZIONI Diffy-S: Learning Robot Operator Schemata from Examples, Pages 430-436, CARL M. KADIE Experimental Results from an Evaluation of Algorithms that Learn to Control Dynamic Systems, Pages 437-443, CLAUDE SAMMUT Utilizing Experience for Improving the Tactical Manager, Pages 444-450, MICHAEL D. ERICKSON, JAN M. ZYTKOW Some Chunks Are Expensive, Pages 451-458, Milind Tambe, Allen Newell The Role of Forgetting in Learning, Pages 459-465, SHAUL MARKOVITCH, PAUL D. SCOTT INDEX, Page 467