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ORCID7th ML 1990: Austin, Texas, USA
- Bruce W. Porter, Raymond J. Mooney:
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 - Marc Schoenauer, Michèle Sebag:
Incremental Learning of Rules and Meta-rules. 49-57 - 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
- John R. Anderson, Michael Matessa:
A Rational Analysis of Categorization. 76-84 - 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 - Céline Rouveirol, Jean-Francois Puget:
Beyond Inversion of Resolution. 122-130
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 - R. 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 - Claude Sammut, James Cribb:
Is Learning Rate a Good Performance Criterion for Learning? 170-178 - Steven D. Whitehead, Dana H. Ballard:
Active Perception and Reinforcement Learning. 179-188
Learning and Planning
- Susan L. Epstein:
Learning Plans for Competitive Domains. 190-197 - Diana F. Gordon, John J. Grefenstette:
Explanations of Empirically Derived Reactive Plans. 198-203 - 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 - Yihua Wu, Shulin Wang, Qing Zhou:
An Integrated Framework of Inducing Rules from Examples. 357-365
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 - Zoran Obradovic, Ian Parberry:
Learning with Discrete Multi-Valued Neurons. 392-399
Other Topics
- Lawrence B. Holder:
The General Utility Problem in Machine Learning. 402-410 - Bernd Nordhausen, Pat Langley:
A Robust Approach to Numeric Discovery. 411-418 - Marco Valtorta:
More Results on the Complexity of Knowledge Base Refinement: Belief Networks. 419-426
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