Machine Learning Proceedings 1993. Proceedings of the Tenth International Conference, University of Massachusetts, Amherst, June 27–29, 1993

Content: 
Front Matter, Page i
Copyright, Page ii
PREFACE, Page vii, Paul Utgoff
ORGANIZING COMMITTEE, Page viii
WORKSHOPS, Page ix
Inside Front Cover, Pages x-xii
The Evolution of Genetic Algorithms: Towards Massive Parallelism, Pages 1-8, Shumeet Baluja
éLéNA: A BOTTOM-UP LEARNING METHOD, Pages 9-16, Pierre Brézellec, Henry Soldano
Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection, Pages 17-24, Carla E. Brodley
Using Decision Trees to Improve Case-Based Learning, Pages 25-32, Claire Cardie
GALOIS : An order-theoretic approach to conceptual clustering, Pages 33-40, Claudio Carpineto, Giovanni Romano
Multitask Learning: A Knowledge-Based Source of Inductive Bias, Pages 41-48, Richard A. Caruana
Using Qualitative Models to Guide Inductive Learning, Pages 49-56, Peter Clark, Stan Matwin
Automating Path Analysis for Building Causal Models from Data, Pages 57-64, Paul R. Cohen, Adam Carlson, Lisa Ballesteros, Robert St.Amant
Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering, Pages 65-72, Dennis Connolly
Learning Symbolic Rules Using Artificial Neural Networks, Pages 73-80, Mark W. Craven, Jude W. Shavlik
Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network, Pages 81-88, Andrea Pohoreckyj Danyluk, Foster John Provost
Concept Sharing: A Means to Improve Multi-Concept Learning, Pages 89-96, Piew Datta, Dennis Kibler
Discovering Dynamics, Pages 97-103, Sašo Džeroski, Ljupčo Todorovski
Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximately Equivalent Objects, Pages 104-111, Thomas Ellman
SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys, Pages 112-119, Usama M. Fayyad, Nicholas Weir, S. Djorgovski
Learning From Entailment: An Application to Propositional Horn Sentences, Pages 120-127, Michael Frazier, Leonard Pitt
Efficient Domain-Independent Experimentation, Pages 128-134, Yolanda Gil
Learning Search Control Knowledge for Deep Space Network Scheduling, Pages 135-142, Jonathan Gratch, Steve Chien, Gerald Dejong
Learning procedures from interactive natural language instructions, Pages 143-150, Scott B. Huffman, John E. Laird
Generalization under Implication by Recursive Anti-unification, Pages 151-158, Peter Idestam-Almquist
Supervised learning and divide-and-conquer: A statistical approach, Pages 159-166, Michael I. Jordan, Robert A. Jacobs
Hierarchical Learning in Stochastic Domains: Preliminary Results, Pages 167-173, Leslie Pack Kaelbling
Constraining Learning with Search Control, Pages 174-181, Jihie Kim, Paul S. Rosenbloom
Scaling Up Reinforcement Learning for Robot Control, Pages 182-189, Long-Ji Lin
Overcoming Incomplete Perception with Utile Distinction Memory, Pages 190-196, R. Andrew McCallum
Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches, Pages 197-204, Tom M. Mitchell, Sebastian B. Thrun
Combinatorial optimization in inductive concept learning, Pages 205-211, Dunja Mladenić
Decision Theoretic Subsampling for Induction on Large Databases, Pages 212-219, Ron Musick, Jason Catlett, Stuart Russell
Learning DNF Via Probabilistic Evidence Combination, Pages 220-227, Steven W. Norton, Haym Hirsh
Explaining and Generalizing Diagnostic Decisions, Pages 228-235, Paul O'Rorke
Combining Instance-Based and Model-Based Learning, Pages 236-243, J.R. Quinlan
Data Mining of Subjective Agricultural Data, Pages 244-251, R. Bharat Rao, Thomas B. Voigt, Thomas W. Fermanian
Lookahead Feature Construction for Learning Hard Concepts, Pages 252-259, Harish Ragavan, Larry Rendell
Adaptive NeuroControl: How Black Box and Simple can it be, Pages 260-267, Jean-Michel Renders, Hugues Bersini, Marco Saerens
An SE-tree based Characterization of the Induction Problem, Pages 268-275, Ron Rymon
Density-Adaptive Learning and Forgetting, Pages 276-283, Marcos Salganicoff
Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal Pruning, Pages 284-290, Jeffrey C. Schlimmer
Compiling Bayesian Networks into Neural Networks, Pages 291-297, Eddie Schwalb
A Reinforcement Learning Method for Maximizing Undiscounted Rewards, Pages 298-305, Anton Schwartz
ATM Scheduling with Queuing Delay Predictions, Pages 306-313, Daniel B. Schwartz
Online Learning with Random Representations, Pages 314-321, Richard S. Sutton, Steven D. Whitehead
Learning from Queries and Examples with Tree-structured Bias, Pages 322-329, Prasad Tadepalli
Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents, Pages 330-337, Ming Tan
Better Learners Use Analogical Problem Solving Sparingly, Pages 338-345, Kurt VanLehn, Randolph M. Jones
AUTHOR INDEX, Page 346
SUBJECT INDEX, Pages 347-348