MLJ 1992

28 papers

A Bayesian Method for the Induction of Probabilistic Networks from Data Gregory F. Cooper, Edward Herskovits

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

A Further Comparison of Splitting Rules for Decision-Tree Induction Wray L. Buntine, Tim Niblett

A Learning Criterion for Stochastic Rules Kenji Yamanishi

A Reinforcement Connectionist Approach to Robot Path Finding in Non-Maze-like Environments José del R. Millán, Carme Torras

A Universal Method of Scientific Inquiry Daniel N. Osherson, Michael Stob, Scott Weinstein

Abductive Explanation-Based Learning: A Solution to the Multiple Inconsistent Explanation Problem William W. Cohen

Dynamic Parameter Encoding for Genetic Algorithms Nicol N. Schraudolph, Richard K. Belew

Explorations of an Incremental, Bayesian Algorithm for Categorization John R. Anderson, Michael Matessa

First Nearest Neighbor Classification on Frey and Slate's Letter Recognition Problem Terence C. Fogarty

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization Scott Dietzen, Frank Pfenning

Implementing Valiant's Learnability Theory Using Random Sets E. M. Oblow

Interactive Concept-Learning and Constructive Induction by Analogy Luc De Raedt, Maurice Bruynooghe

Learning Boolean Functions in an Infinite Attribute Space Avrim Blum

Learning Conjunctions of Horn Clauses Dana Angluin, Michael Frazier, Leonard Pitt

Learning Probabilistic Automata and Markov Chains via Queries Wen-Guey Tzeng

Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System Francesco Bergadano

Lower Bound Methods and Separation Results for On-Line Learning Models Wolfgang Maass, György Turán

Machine Learning: A Maturing Field Jaime G. Carbonell

On the Computational Complexity of Approximating Distributions by Probabilistic Automata Naoki Abe, Manfred K. Warmuth

On the Handling of Continuous-Valued Attributes in Decision Tree Generation Usama M. Fayyad, Keki B. Irani

Practical Issues in Temporal Difference Learning Gerald Tesauro

Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching Long Ji Lin

Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning Ronald J. Williams

Technical Note Q-Learning Christopher J. C. H. Watkins, Peter Dayan

The Convergence of TD(lambda) for General Lambda Peter Dayan

The Utility of Knowledge in Inductive Learning Michael J. Pazzani, Dennis F. Kibler

Transfer of Learning by Composing Solutions of Elemental Sequential Tasks Satinder Pal Singh