Machine Learning 1993 International Conference

• ISBN13: 9781558603073
• ISBN10: 1558603077
• Format: Paperback
• Copyright: 1993-06-01
• Publisher: Elsevier Science Ltd

Table of Contents

Preface
Organizing Committee and Program Committee
Workshops
Schedule
The Evolution of Genetic Algorithms: Towards Massive Parallelism	p. 1
ELENA: A Bottom-Up Learning Method	p. 9
Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection	p. 17
Using Decision Trees to Improve Case-Based Learning	p. 25
GALOIS: An order-theoretic approach to conceptual clustering	p. 33
Multitask Learning: A Knowledge-Based Source of Inductive Bias	p. 41
Using Qualitative Models to Guide Inductive Learning	p. 49
Automating Path Analysis for Building Causal Models from Data	p. 57
Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering	p. 65
Learning Symbolic Rules Using Artificial Neural Networks	p. 73
Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network	p. 81
Concept Sharing: A Means to Improve Multi-Concept Learning	p. 89
Discovering Dynamics	p. 97
Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximately Equivalent Objects	p. 104
SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys	p. 112
Learning From Entailment: An Application to Propositional Horn Sentences	p. 120
Efficient Domain-Independent Experimentation	p. 128
Learning Search Control Knowledge for Deep Space Network Scheduling	p. 135
Learning procedures from interactive natural language instructions	p. 143
Generalization under Implication by Recursive Anti-unification	p. 151
Supervised learning and divide-and-conquer: A statistical approach	p. 159
Hierarchical Learning in Stochastic Domains: Preliminary Results	p. 167
Constraining Learning with Search Control	p. 174
Sealing Up Reinforcement Learning for Robot Control	p. 182
Overcoming Incomplete Perception with Utile Distinction Memory	p. 190
Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches	p. 197
Combinatorial optimization in inductive concept learning	p. 205
Decision Theoretic Subsampling for Induction on Large Databases	p. 212
Learning DNF Via Probabilistic Evidence Combination	p. 220
Explaining and Generalizing Diagnostic Decisions	p. 228
Combining Instance-Based and Model-Based Learning	p. 236
Data Mining of Subjective Agricultural Data	p. 244
Lookahead Feature Construction for Learning Hard Concepts	p. 252
Adaptive NeuroControl: How Black Box and Simple can it be	p. 260
An SE-tree based Characterization of the Induction Problem	p. 268
Density-Adaptive Learning and Forgetting	p. 276
Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal Pruning	p. 284
Compiling Bayesian Networks into Neural Networks	p. 291
A Reinforcement Learning Method for Maximizing Undiscounted Rewards	p. 298
ATM Scheduling with Queuing Delay Predictions	p. 306
Online Learning with Random Representations	p. 314
Learning from Queries and Examples with Tree-structured Bias	p. 322
Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents	p. 330
Better Learners Use Analogical Problem Solving Sparingly	p. 338
Author Index	p. 346
Subject Index	p. 347
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