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

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