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ORCID10th ICML 1993: Amherst, MA, USA
- Paul E. Utgoff:
Machine Learning, Proceedings of the Tenth International Conference, University of Massachusetts, Amherst, MA, USA, June 27-29, 1993. Morgan Kaufmann 1993, ISBN 1-55860-307-7 - Shumeet Baluja:
The Evolution of Gennetic Algorithms: Towards Massive Parallelism. 1-8 - Pierre Brézellec, Henry Soldano:
ÉLÉNA: A Bottom-Up Learning Method. 9-16 - Carla E. Brodley:
Automatic Algorith/Model Class Selection. 17-24 - Claire Cardie:
Using Decision Trees to Improve Case-Based Learning. 25-32 - Claudio Carpineto, Giovanni Romano
:
GALOIS: An Order-Theoretic Approach to Conceptual Clustering. 33-40 - Rich Caruana:
Multitask Learning: A Knowledge-Based Source of Inductive Bias. 41-48 - Peter Clark, Stan Matwin:
Using Qualitative Models to Guide Inductive Learning. 49-56 - Paul R. Cohen, Adam Carlson, Lisa Ballesteros, Adam St. Amant:
Automating Path Analysis for Building Causal Models from Data. 57-64 - Dennis Connolly:
Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering. 65-72 - Mark W. Craven, Jude W. Shavlik:
Learning Symbolic Rules Using Artificial Neural Networks. 73-80 - Andrea Pohoreckyj Danyluk, Foster J. Provost:
Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network. 81-88 - Piew Datta, Dennis F. Kibler:
Concept Sharing: A Means to Improve Multi-Concept Learning. 89-96 - Saso Dzeroski, Ljupco Todorovski:
Discovering Dynamics. 97-103 - Thomas Ellman:
Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximately Equivalent Objects. 104-111 - Usama M. Fayyad, Nicholas Weir, S. George Djorgovski:
SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys. 112-119 - Michael Frazier, Leonard Pitt:
Learning From Entailment: An Application to Propositional Horn Sentences. 120-127 - Yolanda Gil:
Efficient Domain-Independent Experimentation. 128-134 - Jonathan Gratch, Steve A. Chien, Gerald DeJong:
Learning Search Control Knowledge for Deep Space Network Scheduling. 135-142 - Scott B. Huffman, John E. Laird:
Learning Procedures from Interactive Natural Language Instructions. 143-150 - Peter Idestam-Almquist:
Generalization under Implication by Recursive Anti-unification. 151-158 - Michael I. Jordan, Robert A. Jacobs:
Supervised Learning and Divide-and-Conquer: A Statistical Approach. 159-166 - Leslie Pack Kaelbling:
Hierarchical Learning in Stochastic Domains: Preliminary Results. 167-173 - Jihie Kim, Paul S. Rosenbloom:
Constraining Learning with Search Control. 174-181 - Long Ji Lin:
Scaling Up Reinforcement Learning for Robot Control. 182-189 - R. Andrew McCallum:
Overcoming Incomplete Perception with Utile Distinction Memory. 190-196 - Tom M. Mitchell, Sebastian Thrun:
Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches. 197-204 - Dunja Mladenic:
Combinatorial Optimization in Inductive Concept Learning. 205-211 - Ron Musick, Jason Catlett, Stuart Russell:
Decision Theoretic Subsampling for Induction on Large Databases. 212-219 - Steven W. Norton, Haym Hirsh:
Learning DNF Via Probabilistic Evidence Combination. 220-227 - Paul O'Rorke, Yousri El Fattah, Margaret Elliott:
Explaining and Generalizing Diagnostic Decisions. 228-235 - J. Ross Quinlan:
Combining Instance-Based and Model-Based Learning. 236-243 - R. Bharat Rao, Thomas B. Voigt, Thomas W. Fermanian:
Data Mining of Subjective Agricultural Data. 244-251 - Harish Ragavan, Larry A. Rendell:
Lookahead Feature Construction for Learning Hard Concepts. 252-259 - Jean-Michel Renders, Hugues Bersini, Marco Saerens:
Adaptive NeuroControl: How Black Box and Simple can it be. 260-267 - Ron Rymon:
An SE-tree based Characterization of the Induction Problem. 268-275 - Marcos Salganicoff:
Density-Adaptive Learning and Forgetting. 276-283 - Jeffrey C. Schlimmer:
Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal Pruning. 284-290 - Eddie Schwalb:
Compiling Bayesian Networks into Neural Networks. 291-297 - Anton Schwartz:
A Reinforcement Learning Method for Maximizing Undiscounted Rewards. 298-305 - Daniel B. Schwartz:
ATM Scheduling with Queuing Dely Predictions. 306-313 - Richard S. Sutton, Steven D. Whitehead:
Online Learning with Random Representations. 314-321 - Prasad Tadepalli:
Learning from Queries and Examples with Tree-structured Bias. 322-329 - Ming Tan:
Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents. 330-337 - Kurt VanLehn, Randolph M. Jones:
Better Learners Use Analogical Problem Solving Sparingly. 338-345
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