ICML 1994

45 papers

A Bayesian Framework to Integrate Symbolic and Neural Learning Irina Tchoumatchenko, Jean-Gabriel Ganascia

A Conservation Law for Generalization Performance Cullen Schaffer

A Constraint-Based Induction Algorithm in FOL Michèle Sebag

A Modular Q-Learning Architecture for Manipulator Task Decomposition Chen-Khong Tham, Richard W. Prager

A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars Naoki Abe, Hiroshi Mamitsuka

A Powerful Heuristic for the Discovery of Complex Patterned Behaviour Raúl E. Valdés-Pérez, Aurora Pérez

A Statistical Approach to Decision Tree Modeling Michael I. Jordan

An Efficient Subsumption Algorithm for Inductive Logic Programming Jörg-Uwe Kietz, Marcus Lübbe

An Improved Algorithm for Incremental Induction of Decision Trees Paul E. Utgoff

An Incremental Learning Approach for Completable Planning Melinda T. Gervasio, Gerald DeJong

Bayesian Inductive Logic Programming Stephen H. Muggleton

Boosting and Other Machine Learning Algorithms Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik

Combining Top-Down and Bottom-up Techniques in Inductive Logic Programming John M. Zelle, Raymond J. Mooney, Joshua B. Konvisser

Comparing Methods for Refining Certainty-Factor Rule-Bases J. Jeffrey Mahoney, Raymond J. Mooney

Consideration of Risk in Reinforcement Learning Matthias Heger

Efficient Algorithms for Minimizing Cross Validation Error Andrew W. Moore, Mary S. Lee

Frequencies vs. Biases: Machine Learning Problems in Natural Language Processing - Abstract Fernando C. N. Pereira

Getting the Most from Flawed Theories Moshe Koppel, Alberto Maria Segre, Ronen Feldman

Greedy Attribute Selection Rich Caruana, Dayne Freitag

Heterogeneous Uncertainty Sampling for Supervised Learning David D. Lewis, Jason Catlett

Hierarchical Self-Organization in Genetic Programming Justinian P. Rosca, Dana H. Ballard

Improving Accuracy of Incorrect Domain Theories Lars Asker

In Defense of C4.5: Notes in Learning One-Level Decision Trees Tapio Elomaa

Incremental Multi-Step Q-Learning Jing Peng, Ronald J. Williams

Incremental Reduced Error Pruning Johannes Fürnkranz, Gerhard Widmer

Irrelevant Features and the Subset Selection Problem George H. John, Ron Kohavi, Karl Pfleger

Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains Yolanda Gil

Learning Disjunctive Concepts by Means of Genetic Algorithms Attilio Giordana, Lorenza Saitta, Floriano Zini

Learning Recursive Relations with Randomly Selected Small Training Sets David W. Aha, Stephane Lapointe, Charles X. Ling, Stan Matwin

Learning Without State-Estimation in Partially Observable Markovian Decision Processes Satinder P. Singh, Tommi S. Jaakkola, Michael I. Jordan

Markov Games as a Framework for Multi-Agent Reinforcement Learning Michael L. Littman

On the Worst-Case Analysis of Temporal-Difference Learning Algorithms Robert E. Schapire, Manfred K. Warmuth

Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms David B. Skalak

Reducing Misclassification Costs Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal M. Ali, Timothy Hume, Clifford Brunk

Revision of Production System Rule-Bases Patrick M. Murphy, Michael J. Pazzani

Reward Functions for Accelerated Learning Maja J. Mataric

Rule Induction for Semantic Query Optimization Chun-Nan Hsu, Craig A. Knoblock

Selective Reformulation of Examples in Concept Learning Jean-Daniel Zucker, Jean-Gabriel Ganascia

Small Sample Decision Tree Pruning Sholom M. Weiss, Nitin Indurkhya

The Generate, Test, and Explain Discovery System Architecture Michael de la Maza

The Minimum Description Length Principle and Categorical Theories J. Ross Quinlan

To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning Sridhar Mahadevan

Towards a Better Understanding of Memory-Based Reasoning Systems John Rachlin, Simon Kasif, Steven Salzberg, David W. Aha

Using Genetic Search to Refine Knowledge-Based Neural Networks David W. Opitz, Jude W. Shavlik

Using Sampling and Queries to Extract Rules from Trained Neural Networks Mark W. Craven, Jude W. Shavlik