MLJ 1996

40 papers

A Decision-Tree Model of Balance Scale Development William C. Schmidt, Charles X. Ling

A Lattice Conceptual Clustering System and Its Application to Browsing Retrieval Claudio Carpineto, Giovanni Romano

Active Learning for Vision-Based Robot Grasping Marcos Salganicoff, Lyle H. Ungar, Ruzena Bajcsy

Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results Sridhar Mahadevan

Bagging Predictors Leo Breiman

BEXA: A Covering Algorithm for Learning Propositional Concept Descriptions Hendrik Theron, Ian Cloete

Classic Learning Michael Frazier, Leonard Pitt

Classification by Feature Partitioning H. Altay Güvenir, Izzet Sirin

Creating Advice-Taking Reinforcement Learners Richard Maclin, Jude W. Shavlik

Efficient Incremental Induction of Decision Trees Dimitrios Kalles, Tim Morris

Efficient Reinforcement Learning Through Symbiotic Evolution David E. Moriarty, Risto Miikkulainen

Error Reduction Through Learning Multiple Descriptions Kamal M. Ali, Michael J. Pazzani

Exploration Bonuses and Dual Control Peter Dayan, Terrence J. Sejnowski

Feature-Based Methods for Large Scale Dynamic Programming John N. Tsitsiklis, Benjamin Van Roy

Incremental Multi-Step Q-Learning Jing Peng, Ronald J. Williams

Learning Concepts from Sensor Data of a Mobile Robot Volker Klingspor, Katharina Morik, Anke D. Rieger

Learning Controllers for Industrial Robots Cristina Baroglio, Attilio Giordana, Roberto Piola, Michael Kaiser, Marnix Nuttin

Learning in the Presence of Concept Drift and Hidden Contexts Gerhard Widmer, Miroslav Kubat

Linear Least-Squares Algorithms for Temporal Difference Learning Steven J. Bradtke, Andrew G. Barto

On Learning Visual Concepts and DNF Formulae Eyal Kushilevitz, Dan Roth

On the Limits of Proper Learnability of Subclasses of DNF Formulas Krishnan Pillaipakkamnatt, Vijay Raghavan

On the Worst-Case Analysis of Temporal-Difference Learning Algorithms Robert E. Schapire, Manfred K. Warmuth

On-Line Prediction and Conversion Strategies Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, Manfred K. Warmuth

PAC Learning of One-Dimensional Patterns Paul W. Goldberg, Sally A. Goldman, Stephen D. Scott

Performance Improvement of Robot Continuous-Path Operation Through Iterative Learning Using Neural Networks Peter C. Y. Chen, James K. Mills, Kenneth C. Smith

Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning Minoru Asada, Shoichi Noda, Sukoya Tawaratsumida, Koh Hosoda

Real-World Robotics: Learning to Plan for Robust Execution Scott W. Bennett, Gerald DeJong

Reinforcement Learning with Replacing Eligibility Traces Satinder P. Singh, Richard S. Sutton

Representation of Finite State Automata in Recurrent Radial Basis Function Networks Paolo Frasconi, Marco Gori, Marco Maggini, Giovanni Soda

Review of "Inductive Logic Programming: Techniques and Applications" by Nada Lavrac, Saso Dzeroski Michael J. Pazzani

Rigorous Learning Curve Bounds from Statistical Mechanics David Haussler, Michael J. Kearns, H. Sebastian Seung, Naftali Tishby

Robot Programming by Demonstration (RPD): Supporting the Induction by Human Interaction Holger Friedrich, Stefan Münch, Rüdiger Dillmann, Siegfried Bocionek, Michael Sassin

Scaling up Inductive Learning with Massive Parallelism Foster J. Provost, John M. Aronis

Stacked Regressions Leo Breiman

Technical Note: Some Properties of Splitting Criteria Leo Breiman

The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning Algorithms Sven Koenig, Reid G. Simmons

The Loss from Imperfect Value Functions in Expectation-Based and Minimax-Based Tasks Matthias Heger

The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length Dana Ron, Yoram Singer, Naftali Tishby

Unifying Instance-Based and Rule-Based Induction Pedro M. Domingos

Using the Minimum Description Length Principle to Infer Reduced Ordered Decision Graphs Arlindo L. Oliveira, Alberto L. Sangiovanni-Vincentelli