11. ICML 1994:
New Brunswick,
NJ,
USA
William W. Cohen, Haym Hirsh (Eds.):
Machine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994.
Morgan Kaufmann 1994, ISBN 1-55860-335-2 
Contributed Papers
- Naoki Abe, Hiroshi Mamitsuka:
A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars.
3-11
- David W. Aha, Stephane Lapointe, Charles X. Ling, Stan Matwin:
Learning Recursive Relations with Randomly Selected Small Training Sets.
12-18
- Lars Asker:
Improving Accuracy of Incorrect Domain Theories.
19-27
- Rich Caruana, Dayne Freitag:
Greedy Attribute Selection.
28-36
- Mark Craven, Jude W. Shavlik:
Using Sampling and Queries to Extract Rules from Trained Neural Networks.
37-45
- Michael de la Maza:
The Generate, Test, and Explain Discovery System Architecture.
46-52
- Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik:
Boosting and Other Machine Learning Algorithms.
53-61
- Tapio Elomaa:
In Defense of C4.5: Notes Learning One-Level Decision Trees.
62-69
- Johannes Fürnkranz, Gerhard Widmer:
Incremental Reduced Error Pruning.
70-77
- Melinda T. Gervasio, Gerald DeJong:
An Incremental Learning Approach for Completable Planning.
78-86
- Yolanda Gil:
Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains.
87-95
- Attilio Giordana, Lorenza Saitta, Floriano Zini:
Learning Disjunctive Concepts by Means of Genetic Algorithms.
96-104
- Matthias Heger:
Consideration of Risk in Reinformance Learning.
105-111
- Chun-Nan Hsu, Craig A. Knoblock:
Rule Introduction for Semantic Query Optimization.
112-120
- George H. John, Ron Kohavi, Karl Pfleger:
Irrelevant Features and the Subset Selection Problem.
121-129
- Jörg-Uwe Kietz, Marcus Lübbe:
An Efficient Subsumption Algorithm for Inductive Logic Programming.
130-138
- Moshe Koppel, Alberto Maria Segre, Ronen Feldman:
Getting the Most from Flawed Theories.
139-147
- David D. Lewis, Jason Catlett:
Heterogenous Uncertainty Sampling for Supervised Learning.
148-156
- Michael L. Littman:
Markov Games as a Framework for Multi-Agent Reinforcement Learning.
157-163
- Sridhar Mahadevan:
To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning.
164-172
- J. Jeffrey Mahoney, Raymond J. Mooney:
Comparing Methods for Refining Certainty-Factor Rule-Bases.
173-180
- Maja J. Mataric:
Reward Functions for Accelerated Learning.
181-189
- Andrew W. Moore, Mary S. Lee:
Efficient Algorithms for Minimizing Cross Validation Error.
190-198
- Patrick M. Murphy, Michael J. Pazzani:
Revision of Production System Rule-Bases.
199-207
- David W. Opitz, Jude W. Shavlik:
Using Genetic Search to Refine Knowledge-based Neural Networks.
208-216
- Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal Ali, Timothy Hume, Clifford Brunk:
Reducing Misclassification Costs.
217-225
- Jing Peng, Ronald J. Williams:
Incremental Multi-Step Q-Learning.
226-232
- J. Ross Quinlan:
The Minimum Description Length Principle and Categorical Theories.
233-241
- John Rachlin, Simon Kasif, Steven Salzberg, David W. Aha:
Towards a Better Understanding of Memory-based Reasoning Systems.
242-250
- Justinian P. Rosca, Dana H. Ballard:
Hierarchical Self-Organization in Genetic programming.
251-258
- Cullen Schaffer:
A Conservation Law for Generalization Performance.
259-265
- Robert E. Schapire, Manfred K. Warmuth:
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms.
266-274
- Michèle Sebag:
A Constraint-based Induction Algorithm in FOL.
275-283
- Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan:
Learning Without State-Estimation in Partially Observable Markovian Decision Processes.
284-292
- David B. Skalak:
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms.
293-301
- Irina Tchoumatchenko, Jean-Gabriel Ganascia:
A Baysian Framework to Integrate Symbolic and Neural Learning.
302-308
- Chen K. Tham, Richard W. Prager:
A Modular Q-Learning Architecture for Manipulator Task Decomposition.
309-317
- Paul E. Utgoff:
An Improved Algorithm for Incremental Induction of Decision Trees.
318-325
- Raúl E. Valdés-Pérez, Aurora Pérez:
A Powerful Heuristic for the Discovery of Complex Patterned Behaviour.
326-334
- Sholom M. Weiss, Nitin Indurkhya:
Small Sample Decision tree Pruning.
335-342
- John M. Zelle, Raymond J. Mooney, Joshua B. Konvisser:
Combining Top-down and Bottom-up Techniques in Inductive Logic Programming.
343-351
- Jean-Daniel Zucker, Jean-Gabriel Ganascia:
Selective Reformulation of Examples in Concept Learning.
352-360
Invited Talks
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