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
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
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