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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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 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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