7. ML 1990: Austin, Texas, USA
Bruce W. Porter, Raymond J. Mooney (Eds.): Machine Learning, Proceedings of the Seventh International Conference on Machine Learning, Austin, Texas, USA, June 21-23, 1990. Morgan Kaufmann 1990 ISBN 1-55860-141-4
Empirical Learning
- S. Arunkumar, S. Yegneshwar: Knowledge Acquisition from Examples using Maximal Representation Learning. 2-8
- Gilles Bisson: KBG : A Knowledge Based Generalizer. 9-15
- Keith C. C. Chan, Andrew K. C. Wong: Performance Analysis of a Probabilistic Inductive Learning System. 16-23
- Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri: A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping. 24-31
- Haym Hirsh: Learning from Data with Bounded Inconsistency. 32-39
- Carl Myers Kadie: Conceptual Set Covering: Improving Fit-And-Split Algorithms. 40-48
- Paul E. Utgoff, Carla E. Brodley: An Incremental Method for Finding Multivariate Splits for Decision Trees. 58-65
- Walter Van de Velde: Incremental Induction of Topologically Minimal Trees. 66-74
Conceptual Clustering
- Brian M. Carlson, Jerry B. Weinberg, Douglas H. Fisher: Search Control, Utility, and Concept Induction. 85-92
- Jakub Segen: Graph Clustering and Model Learning by Data Compression. 93-101
Constructive Induction and Reformulation
- William W. Cohen: An Analysis of Representation Shift in Concept Learning. 104-112
- David V. Hume: Learning Procedures by Environment-Driven Constructive Induction. 113-121
Genetic Algorithms
- Hugo de Garis: Genetic Programming. 132-139
- Nagesh Kadaba, Kendall E. Nygard: Improving the Performance of Genetic Algorithms in Automated Discovery of Parameters. 140-148
- Andrew McCallum, Kent A. Spackman: Using Genetic Algorithms to Learn Disjunctive Rules from Examples. 149-152
- Pierre Bonelli, Alexandre Parodi, Sandip Sen, Stewart W. Wilson: Newboole: A Fast GBML System. 153-159
Neural Network & Reinforcement Learning
- Leslie Pack Kaelbling: Learning Functions in k-DNF from Reinforcement. 162-169
Learning and Planning
- Susan L. Epstein: Learning Plans for Competitive Domains. 190-197
- Kristian J. Hammond: Learning and Enforcement: Stabilizing Environments to Facilitate Activity. 204-210
- Connie Loggia Ramsey, John J. Grefenstette, Alan C. Schultz: Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment. 211-215
- Richard S. Sutton: Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming. 216-224
Robot Learning
- Scott W. Bennett: Reducing Real-world Failures of Approximate Explanation-based Rules. 226-234
- John E. Laird, Michael Hucka, Eric S. Yager, Christopher M. Tuck: Correcting and Extending Domain Knowledge using Outside Guidance. 235-243
- Andrew W. Moore: Acquisition of Dynamic Control Knowledge for a Robotic Manipulator. 244-252
- Marcus Thint, Paul P. Wang: Feature Extraction and Clustering of Tactile Impressions with Connectionist Models. 253-258
Explanation-Based Learning
- Henrik Boström: Generalizing the Order of Goals as an Approach to Generalizing Number. 260-267
- William W. Cohen: Learning Approximate Control Rules of High Utility. 268-276
- Nicholas S. Flann: Applying Abstraction and Simplification to Learn in Intractable Domains. 277-285
- Jean Genest, Stan Matwin, Boris Plante: Explanation-Based Learning with Incomplete Theories: A Three-step Approach. 286-294
- Yves Kodratoff: Using Abductive Recovery of Failed Proofs for Problem Solving by Analogy. 295-303
- Steven Minton: Issues in the Design of Operator Composition Systems. 304-312
- Ashwin Ram: Incremental Learning of Explanation Patterns and Their Indices. 313-320
Explanation-Based and Empirical Learning
- Francesco Bergadano, Attilio Giordana, Lorenza Saitta, Davide De Marchi, Filippo Brancadori: Integrated Learning in a real Domain. 322-329
- Haym Hirsh: Incremental Version-Space Merging. 330-338
- Michael J. Pazzani, Wendy Sarrett: Average Case Analysis of Conjunctive Learning Algorithms. 339-347
- Bernard Silver, William J. Frawley, Glenn A. Iba, John Vittal, Kelly Bradford: A Framework for Multi-Paradigmatic Learning. 348-356
Language Learning
- Jill Fain Lehman: A General Method for Learning Idiosyncratic Grammars. 368-376
- Steven L. Lytinen, Carol E. Moon: A Comparison of Learning Techniques in Second Language Learning. 377-383
- Ker-I Ko, Assaf Marron, Wen-Guey Tzeng: Learning String Patterns and Tree Patterns from Examples. 384-391
Other Topics
- Lawrence B. Holder: The General Utility Problem in Machine Learning. 402-410
- Marco Valtorta: More Results on the Complexity of Knowledge Base Refinement: Belief Networks. 419-426
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last updated on 2013-06-17T203624 by the dblp team