ICML 1990

50 papers

A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri

A Comparison of Learning Techniques in Second Language Learning Steven L. Lytinen, Carol E. Moon

A Framework for Multi-Paradigmatic Learning Bernard Silver, William J. Frawley, Glenn A. Iba, John Vittal, Kelly Bradford

A General Method for Learning Idiosyncratic Grammars Jill Fain Lehman

A Rational Analysis of Categorization John R. Anderson, Michael Matessa

A Robust Approach to Numeric Discovery Bernd Nordhausen, Pat Langley

Acquisition of Dynamic Control Knowledge for a Robotic Manipulator Andrew W. Moore

Active Perception and Reinforcement Learning Steven D. Whitehead, Dana H. Ballard

An Analysis of Representation Shift in Concept Learning William W. Cohen

An Incremental Method for Finding Multivariate Splits for Decision Trees Paul E. Utgoff, Carla E. Brodley

An Integrated Framework of Inducing Rules from Examples Yihua Wu, Shulin Wang, Qing Zhou

Applying Abstraction and Simplification to Learn in Intractable Domains Nicholas S. Flann

Average Case Analysis of Conjunctive Learning Algorithms Michael J. Pazzani, Wendy Sarrett

Beyond Inversion of Resolution Céline Rouveirol, Jean-Francois Puget

Conceptual Set Covering: Improving Fit-and-Split Algorithms Carl Myers Kadie

Correcting and Extending Domain Knowledge Using Outside Guidance John E. Laird, Michael Hucka, Eric S. Yager, Christopher M. Tuck

Explanation-Based Learning with Incomplete Theories: A Three-Step Approach Jean Genest, Stan Matwin, Boris Plante

Explanations of Empirically Derived Reactive Plans Diana F. Gordon, John J. Grefenstette

Feature Extraction and Clustering of Tactile Impressions with Connectionist Models Marcus Thint, Paul P. Wang

Generalizing the Order of Goals as an Approach to Generalizing Number Henrik Boström

Genetic Programming Hugo de Garis

Graph Clustering and Model Learning by Data Compression Jakub Segen

Improving the Performance of Genetic Algorithms in Automated Discovery of Parameters Nagesh Kadaba, Kendall E. Nygard

Incremental Induction of Topologically Minimal Trees Walter Van de Velde

Incremental Learning of Explanation Patterns and Their Indices Ashwin Ram

Incremental Learning of Rules and Meta-Rules Marc Schoenauer, Michèle Sebag

Incremental Version-Space Merging Haym Hirsh

Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming Richard S. Sutton

Integrated Learning in a Real Domain Francesco Bergadano, Attilio Giordana, Lorenza Saitta, Davide De Marchi, Filippo Brancadori

Is Learning Rate a Good Performance Criterion for Learning? Claude Sammut, James Cribb

Issues in the Design of Operator Composition Systems Steven Minton

KBG : A Knowledge Based Generalizer Gilles Bisson

Knowledge Acquisition from Examples Using Maximal Representation Learning S. Arunkumar, S. Yegneshwar

Learning and Enforcement: Stabilizing Environments to Facilitate Activity Kristian J. Hammond

Learning Approximate Control Rules of High Utility William W. Cohen

Learning from Data with Bounded Inconsistency Haym Hirsh

Learning Functions in K-DNF from Reinforcement Leslie Pack Kaelbling

Learning Plans for Competitive Domains Susan L. Epstein

Learning Procedures by Environment-Driven Constructive Induction David V. Hume

Learning String Patterns and Tree Patterns from Examples Ker-I Ko, Assaf Marron, Wen-Guey Tzeng

Learning with Discrete Multi-Valued Neurons Zoran Obradovic, Ian Parberry

More Results on the Complexity of Knowledge Base Refinement: Belief Networks Marco Valtorta

Newboole: A Fast GBML System Pierre Bonelli, Alexandre Parodi, Sandip Sen, Stewart W. Wilson

Performance Analysis of a Probabilistic Inductive Learning System Keith C. C. Chan, Andrew K. C. Wong

Reducing Real-World Failures of Approximate Explanation-Based Rules Scott W. Bennett

Search Control, Utility, and Concept Induction Brian M. Carlson, Jerry B. Weinberg, Douglas H. Fisher

Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment Connie Loggia Ramsey, John J. Grefenstette, Alan C. Schultz

The General Utility Problem in Machine Learning Lawrence B. Holder

Using Abductive Recovery of Failed Proofs for Problem Solving by Analogy Yves Kodratoff

Using Genetic Algorithms to Learn Disjunctive Rules from Examples R. Andrew McCallum, Kent A. Spackman