ICML 1993

44 papers

A Reinforcement Learning Method for Maximizing Undiscounted Rewards Anton Schwartz

Adaptive NeuroControl: How Black Box and Simple Can It Be Jean-Michel Renders, Hugues Bersini, Marco Saerens

An SE-Tree Based Characterization of the Induction Problem Ron Rymon

ATM Scheduling with Queuing Dely Predictions Daniel B. Schwartz

Automatic Algorith/Model Class Selection Carla E. Brodley

Automating Path Analysis for Building Causal Models from Data Paul R. Cohen, Adam Carlson, Lisa Ballesteros, Adam St. Amant

Better Learners Use Analogical Problem Solving Sparingly Kurt VanLehn, Randolph M. Jones

Combinatorial Optimization in Inductive Concept Learning Dunja Mladenic

Combining Instance-Based and Model-Based Learning J. Ross Quinlan

Compiling Bayesian Networks into Neural Networks Eddie Schwalb

Concept Sharing: A Means to Improve Multi-Concept Learning Piew Datta, Dennis F. Kibler

Constraining Learning with Search Control Jihie Kim, Paul S. Rosenbloom

Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering Dennis Connolly

Data Mining of Subjective Agricultural Data R. Bharat Rao, Thomas B. Voigt, Thomas W. Fermanian

Decision Theoretic Subsampling for Induction on Large Databases Ron Musick, Jason Catlett, Stuart Russell

Density-Adaptive Learning and Forgetting Marcos Salganicoff

Discovering Dynamics Saso Dzeroski, Ljupco Todorovski

Efficient Domain-Independent Experimentation Yolanda Gil

Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm That Uses Optimal Pruning Jeffrey C. Schlimmer

ÉLÉNA: A Bottom-up Learning Method Pierre Brézellec, Henry Soldano

Explaining and Generalizing Diagnostic Decisions Paul O'Rorke, Yousri El Fattah, Margaret Elliott

Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches Tom M. Mitchell, Sebastian Thrun

GALOIS: An Order-Theoretic Approach to Conceptual Clustering Claudio Carpineto, Giovanni Romano

Generalization Under Implication by Recursive Anti-Unification Peter Idestam-Almquist

Hierarchical Learning in Stochastic Domains: Preliminary Results Leslie Pack Kaelbling

Learning DNF via Probabilistic Evidence Combination Steven W. Norton, Haym Hirsh

Learning from Entailment: An Application to Propositional Horn Sentences Michael Frazier, Leonard Pitt

Learning from Queries and Examples with Tree-Structured Bias Prasad Tadepalli

Learning Procedures from Interactive Natural Language Instructions Scott B. Huffman, John E. Laird

Learning Search Control Knowledge for Deep Space Network Scheduling Jonathan Gratch, Steve A. Chien, Gerald DeJong

Learning Symbolic Rules Using Artificial Neural Networks Mark W. Craven, Jude W. Shavlik

Lookahead Feature Construction for Learning Hard Concepts Harish Ragavan, Larry A. Rendell

Multi-Agent Reinforcement Learning: Independent Versus Cooperative Agents Ming Tan

Multitask Learning: A Knowledge-Based Source of Inductive Bias Rich Caruana

Online Learning with Random Representations Richard S. Sutton, Steven D. Whitehead

Overcoming Incomplete Perception with Utile Distinction Memory R. Andrew McCallum

Scaling up Reinforcement Learning for Robot Control Long Ji Lin

SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys Usama M. Fayyad, Nicholas Weir, S. George Djorgovski

Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network Andrea Pohoreckyj Danyluk, Foster J. Provost

Supervised Learning and Divide-and-Conquer: A Statistical Approach Michael I. Jordan, Robert A. Jacobs

Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximately Equivalent Objects Thomas Ellman

The Evolution of Gennetic Algorithms: Towards Massive Parallelism Shumeet Baluja

Using Decision Trees to Improve Case-Based Learning Claire Cardie

Using Qualitative Models to Guide Inductive Learning Peter Clark, Stan Matwin