Abstract is missing.
- Using a Generalization Hierarchy to Learn from ExamplesRandy Kerber. 1-7
- Tuning Rule-Based Systems to Their EnvironmentsHans Tallis. 8-14
- On Asking the Right QuestionsBrent J. Krawchuk, Ian H. Witten. 15-21
- Concept Simplification and Prediction AccuracyDouglas H. Fisher, Jeffrey C. Schlimmer. 22-28
- Learning Graph Models of ShapeJakub Segen. 29-35
- Learning Categorical Decision Criteria in Biomedical DomainsKent A. Spackman. 36-46
- Conceptual Clumping of Binary Vectors with Occam s RazorJakub Segen. 47-53
- AutoClass: A Bayesian Classification SystemPeter Cheeseman, James Kelly, Matthew Self, John Stutz, Will Taylor, Don Freeman. 54-64
- Trading Off Simplicity and Coverage in Incremental concept LearningWayne Iba, James Wogulis, Pat Langley. 73-79
- Deferred Commitment in UNIMEM: Waiting to LearnMichael Lebowitz. 80-86
- Experiments on the Costs and Benefits of Windowing in ID3Jarryl Wirth, Jason Catlett. 87-99
- Improved Decision Trees: A Generalized Version of ID3Jie Cheng, Usama M. Fayyad, Keki B. Irani, Zhaogang Qian. 100-106
- Using Weighted Networks to Represent Classification Knowledge in Noisy DomainsMing Tan, Larry J. Eshelman. 121-134
- An Empirical Comparison of Genetic and Decision-Tree ClassifiersJ. Ross Quinlan. 135-141
- Population Size in classifier SystemsGeorge G. Robertson. 142-152
- Representation and Hidden Bias: Gray vs. Binary Coding for Genetic AlgorithmsRich Caruana, J. David Schaffer. 153-161
- Classifier Systems with Hamming WeightsLawrence Davis, David K. Young. 162-173
- Midgard: A Genetic Approach to Adaptive Load Balancing for Distributed SystemsAdrian V. Sannier II, Erik D. Goodman. 174-180
- Some Interesting Properties of a Connectionist Inductive Learning SystemEdward J. Wisniewski, James A. Anderson. 181-187
- Connectionist Learning of Expert Backgammon EvaluationsGerald Tesauro. 200-206
- Building and Using Mental Models in a Sensory-Motor DomainBartlett W. Mel. 207-213
- Reasoning about Operationality for Explanation-Based LearningHaym Hirsh. 214-220
- Boundaries of OperationalityMichael S. Braverman, Stuart J. Russell. 221-234
- On the Tractability of Learning from Incomplete TheoriesSridhar Mahadevan, Prasad Tadepalli. 235-241
- Active Explanation Reduction: An Approach to the Multiple Explanations ProblemShankar A. Rajamoney, Gerald DeJong. 242-255
- Generalizing Number and Learning from Multiple Examples in Explanation Based LearningWilliam W. Cohen. 256-269
- Generalizing the Order of Operators in Macro-OperatorsRaymond J. Mooney. 270-283
- Using Experience-Based Learning in Game PlayingKenneth A. De Jong, Alan C. Schultz. 284-290
- Integrated Learning with Incorrect and Incomplete TheoriesMichael J. Pazzani. 291-297
- An Approach Based on Integrated Learning to Generating StoriesClaudio Carpineto. 298-304
- A Knowledge Intensive Approach to Concept InductionFrancesco Bergadano, Attilio Giordana. 305-317
- Theory Discovery and the Hypothesis LanguageKevin T. Kelly. 325-338
- Machine Invention of First Order Predicates by Inverting ResolutionStephen Muggleton, Wray L. Buntine. 339-352
- The Interdependencies of Theory Formation, Revision, and ExperimentationBrian Falkenhainer, Shankar A. Rajamoney. 353-366
- A Hill-Climbing Approach to Machine DiscoveryDonald Rose, Pat Langley. 367-373
- Learning Systems of First-Order RulesNicolas Helft. 395-401
- Two New Frameworks for LearningBalas K. Natarajan, Prasad Tadepalli. 402-415
- Hypothesis Filtering: A Practical Approach to Reliable LearningOren Etzioni. 416-429
- Diffy-S: Learning Robot Operator Schemata from ExamplesCarl Myers Kadie. 430-436
- Experimental Results from an Evaluation of Algorithms that Learn to Control Dynamic SystemsClaude Sammut. 437-443
- Utilizing Experience for Improving the Tactical ManagerMichael D. Erickson, Jan M. Zytkow. 444-450
- Some Chunks Are ExpensiveMilind Tambe, Allen Newell. 451-458
- The Role of Forgetting in LearningShaul Markovitch, Paul D. Scott. 459-465