ICML 1996

66 papers

A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning Rémi Munos

A Generalized Reinforcement-Learning Model: Convergence and Applications Michael L. Littman, Csaba Szepesvári

A Probabilistic Approach to Feature Selection - A Filter Solution Huan Liu, Rudy Setiono

A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geormetric Patterns Sally A. Goldman, Stephen D. Scott

Actual Return Reinforcement Learning Versus Temporal Differences: Some Theoretical and Experimental Results Mark D. Pendrith, Malcolm R. K. Ryan

Algorithms and Applications for Multitask Learning Rich Caruana

An Advanced Evolution Should Not Repeat Its past Errors Caroline Ravise, Michèle Sebag

Analogy Access by Mapping Spreading and Abstraction in Large, Multifunctional Knowledge Bases Davide Roverso

Applying the Multiple Cause Mixture Model to Text Categorization Mehran Sahami, Marti A. Hearst, Eric Saund

Applying the Waek Learning Framework to Understand and Improve C4.5 Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour

Applying Winnow to Context-Sensitive Spelling Correction Andrew R. Golding, Dan Roth

Approximate Value Trees in Structured Dynamic Programming Craig Boutilier, Richard Dearden

Background Knowledge in GA-Based Concept Learning Jukka Hekanaho

Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier Pedro M. Domingos, Michael J. Pazzani

Bias Plus Variance Decomposition for Zero-One Loss Functions Ron Kohavi, David H. Wolpert

Causal Discovery via MML Chris S. Wallace, Kevin B. Korb, Honghua Dai

Constructive Induction Using Fragmentary Knowledge Steven K. Donoho, Larry A. Rendell

Data Mining and Machine Learning (Abstract) Heikki Mannila

Delaying the Choice of Bias: A Disjunctive Version Space Approach Michèle Sebag

Discovering Structure in Multiple Learning Tasks: The TC Algorithm Sebastian Thrun, Joseph O'Sullivan

Discretizing Continuous Attributes While Learning Bayesian Networks Nir Friedman, Moisés Goldszmidt

Efficient Learning of Selective Bayesian Network Classifiers Moninder Singh, Gregory M. Provan

Experimental Knowledge Acquisition for Planning Kang Soo Tae, Diane J. Cook

Experiments with a New Boosting Algorithm Yoav Freund, Robert E. Schapire

Exploiting the Omission of Irrelevant Data Russell Greiner, Adam J. Grove, Alexander Kogan

Identifying the Information Contained in a Flawed Theory Sean P. Engelson, Moshe Koppel

Improving the Efficiency of Knowledge Base Refinement Leonardo Carbonara, Derek H. Sleeman

K Nearest Neighbor Classification on Feature Projections Aynur Akkus, H. Altay Güvenir

Learning Active Classifiers Russell Greiner, Adam J. Grove, Dan Roth

Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique Joe Suzuki

Learning Despite Concept Variation by Finding Structure in Attribute-Based Data Eduardo Pérez, Larry A. Rendell

Learning Evaluation Functions for Large Acyclic Domains Justin A. Boyan, Andrew W. Moore

Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management Kazuo J. Ezawa, Moninder Singh, Steven W. Norton

Learning Radial Basis Function Networks On-Line Enrico Blanzieri, Patrick Katenkamp

Learning Relational Concepts with Decision Trees Peter Geibel, Fritz Wysotzki

Learning Word Association Norms Using Tree Cut Pair Models Naoki Abe, Hang Li

Negative Robust Learning Results from Horn Claus Programs Pascal Jappy, Richard Nock, Olivier Gascuel

Non Mean Square Error Criteria for the Training of Learning Machines Marco Saerens

Non-Linear Decision Trees - NDT Andreas Ittner, Michael Schlosser

Nonparametric Statistical Methods for Experimental Evaluations of Speedup Learning Geoffrey J. Gordon, Alberto Maria Segre

On the Learnability of the Uncomputable Richard H. Lathrop

On-Line Adaptation of a Signal Predistorter Through Dual Reinforcement Learning Patrick Goetz, Shailesh Kumar, Risto Miikkulainen

On-Line Portfolio Selection Using Multiplicative Updates David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth

Passive Distance Learning for Robot Navigation Sven Koenig, Reid G. Simmons

Prababilistic Instance-Based Learning Henry Tirri, Petri Kontkanen, Petri Myllymäki

Recognition and Exploitation of Contextual CLues via Incremental Meta-Learning Gerhard Widmer

Reinforcement Learning in Factories: The Auton Project (Abstract) Andrew W. Moore

Relational Instance-Based Learning Werner Emde, Dietrich Wettschereck

Representation Changes for Efficient Learning in Structural Domains Jean-Daniel Zucker, Jean-Gabriel Ganascia

Representing and Learning Quality-Improving Search Control Knowledge M. Alicia Pérez

Residual Q-Learning Applied to Visual Attention Cesar Bandera, Francisco J. Vico, José Manuel Bravo, Mance E. Harmon, Leemon C. Baird Iii

Scaling up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function Prasad Tadepalli, DoKyeong Ok

Searching for Structure in Multiple Streams of Data Tim Oates, Paul R. Cohen

Second Tier for Decision Trees Miroslav Kubat

Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning Sridhar Mahadevan

Simplified Support Vector Decision Rules Christopher J. C. Burges

Solving POMDPs with Levin Search and EIRA Marco A. Wiering, Jürgen Schmidhuber

Speeding-up Nearest Neighbour Memories: The Template Tree Case Memory Organisation Stephan Grolimund, Jean-Gabriel Ganascia

Statistical Theory of Generalization (Abstract) Vladimir Vapnik

The Characterisation of Predictive Accuracy and Decision Combination Kai Ming Ting

Theoretical Analysis of the Nearest Neighbor Classifier in Noisy Domains Seishi Okamoto, Nobuhiro Yugami

Theory-Guided Empirical Speedup Learning of Goal Decomposition Rules Chandra Reddy, Prasad Tadepalli, Silvana Roncagliolo

Theory-Guideed Induction of Logic Programs by Inference of Regular Languages Henrik Boström

Toward a Model of Mind as a Laissez-Faire Economy of Idiots Eric B. Baum

Toward Optimal Feature Selection Daphne Koller, Mehran Sahami

Unsupervised Learning Using MML Jonathan J. Oliver, Rohan A. Baxter, Chris S. Wallace