Machine Learning Proceedings 1988

Empirical Learning Using a Generalization Hierarchy to Learn from Examples Tuning Rule-Based Systems to their Environments On Asking the Right Questions Concept Simplification and Prediction Accuracy Learning Graph Models of Shape Learning Categorical Decision Criteria in Biomedical Domains Conceptual Clumping of Binary Vectors with Occam's Razor AutoClass: A Bayesian Classification System Incremental Multiple Concept Learning Using Experiments Trading Off Simplicity and Coverage in Incremental Concept Learning Deferred Commitment in UNIMEM: Waiting to Learn Experiments on the Costs and Benefits of Windowing in ID3 Improved Decision Trees: A Generalized Version of ID3 ID5: An Incremental ID3 Using Weighted Networks to Represent Classification Knowledge in Noisy DomainsGenetic Learning An Empirical Comparison of Genetic and Decision-Tree Classifiers Population Size in Classifier Systems Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms Classifier Systems with Hamming Weights Midgard: A Genetic Approach to Adaptive Load Balancing for Distributed SystemsConnectionist Learning Some Interesting Properties of a Connectionist Inductive Learning System Competitive Reinforcement Learning Connectionist Learning of Expert Backgammon Evaluations Building and Using Mental Models in a Sensory-Motor Domain: A Connectionist ApproachExplanation-Based Learning Reasoning about Operationality for Explanation-Based Learning Boundaries of Operationality On the Tractability of Learning from Incomplete Theories Active Explanation Reduction: An Approach to the Multiple Explanations Problem Generalizing Number and Learning from Multiple Examples in Explanation Based Learning Generalizing the Order of Operators in Macro-Operators Using Experience-Based Learning in Game PlayingIntegrated Explanation-Based and Empirical Learning Integrated Learning with Incorrect and Incomplete Theories An Approach Based on Integrated Learning to Generating Stories from Stories A Knowledge Intensive Approach to Concept InductionCase-Based Learning Learning to Program by Examining and Modifying CasesMachine Discovery Theory Discovery and the Hypothesis Language Machine Invention of First Order Predicates by Inverting Resolution The Interdependencies of Theory Formation, Revision, and Experimentation A Hill-Climbing Approach to Machine Discovery Reduction: A Practical Mechanism of Searching for Regularity in DataFormal Models of Concept Learning Extending the Valiant Learning Model Learning Systems of First-Order Rules Two New Frameworks for Learning Hypothesis Filtering: A Practical Approach to Reliable LearningExperimental Results in Machine Learning Diffy-S: Learning Robot Operator Schemata from Examples Experimental Results from an Evaluation of Algorithms that Learn to Control Dynamic Systems Utilizing Experience for Improving the Tactical ManagerComputational Impact of Learning and Forgetting Some Chunks Are Expensive The Role of Forgetting in Learning