10. ICML 1993:
Amherst, MA, USA
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 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
- Andrew McCallum:
Overcoming Incomplete Perception with Util 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 J. 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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