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ORCID11th ICML 1994: New Brunswick, NJ, USA
- William W. Cohen, Haym Hirsh:
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 W. 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 in 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 Reinforcement Learning. 105-111 - Chun-Nan Hsu, Craig A. Knoblock:
Rule Induction 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:
Heterogeneous 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 M. 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 S. 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 Bayesian Framework to Integrate Symbolic and Neural Learning. 302-308 - Chen-Khong 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
- Michael I. Jordan:
A Statistical Approach to Decision Tree Modeling. 363-370 - Stephen H. Muggleton:
Bayesian Inductive Logic Programming. 371-379 - Fernando C. N. Pereira:
Frequencies vs. Biases: Machine Learning Problems in Natural Language Processing - Abstract. 380
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