ICML 2001

80 papers

A General Method for Scaling up Machine Learning Algorithms and Its Application to Clustering Pedro M. Domingos, Geoff Hulten

A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal Difference Learning David Choi, Benjamin Van Roy

A Multi-Agent Policy-Gradient Approach to Network Routing Nigel Tao, Jonathan Baxter, Lex Weaver

A Procedure for Unsupervised Lexicon Learning Anand Venkataraman

A Theory-Refinement Approach to Information Extraction Tina Eliassi-Rad, Jude W. Shavlik

A Unified Loss Function in Bayesian Framework for Support Vector Regression Wei Chu, S. Sathiya Keerthi, Chong Jin Ong

Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a Multi-Class Problem in Remote Sensing Patrice Latinne, Marco Saerens, Christine Decaestecker

An Efficient Approach for Approximating Multi-Dimensional Range Queries and Nearest Neighbor Classification in Large Datasets Carlotta Domeniconi, Dimitrios Gunopulos

An Improved Predictive Accuracy Bound for Averaging Classifiers John Langford, Matthias W. Seeger, Nimrod Megiddo

Application of Fuzzy Similarity-Based Fractal Dimensions to Characterize Medical Time Series Manish Sarkar, Tze-Yun Leong

Automatic Discovery of Subgoals in Reinforcement Learning Using Diverse Density Amy McGovern, Andrew G. Barto

Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems Makoto Sato, Shigenobu Kobayashi

Bayesian Approaches to Failure Prediction for Disk Drives Greg Hamerly, Charles Elkan

Bias Correction in Classification Tree Construction Alin Dobra, Johannes Gehrke

Boosting Neighborhood-Based Classifiers Marc Sebban, Richard Nock, Stéphane Lallich

Boosting Noisy Data Abba Krieger, Chuan Long, Abraham J. Wyner

Boosting with Confidence Information Craig W. Codrington

Breeding Decision Trees Using Evolutionary Techniques Athanassios Papagelis, Dimitrios Kalles

Clustering Continuous Time Series Paola Sebastiani, Marco Ramoni

Collaborative Learning and Recommender Systems Wee Sun Lee

Composite Kernels for Hypertext Categorisation Thorsten Joachims, Nello Cristianini, John Shawe-Taylor

Comprehensible Interpretation of Relief's Estimates Marko Robnik-Sikonja, Igor Kononenko

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira

Constrained K-Means Clustering with Background Knowledge Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schrödl

Continuous-Time Hierarchical Reinforcement Learning Mohammad Ghavamzadeh, Sridhar Mahadevan

Convergence of Gradient Dynamics with a Variable Learning Rate Michael H. Bowling, Manuela M. Veloso

Convergence Rates of the Voting Gibbs Classifier, with Application to Bayesian Feature Selection Andrew Y. Ng, Michael I. Jordan

Coupled Clustering: A Method for Detecting Structural Correspondence Zvika Marx, Ido Dagan, Joachim M. Buhmann

Direct Policy Search Using Paired Statistical Tests Malcolm J. A. Strens, Andrew W. Moore

Discovering Communicable Scientific Knowledge from Spatio-Temporal Data Mark Schwabacher, Pat Langley

Efficient Algorithms for Decision Tree Cross-Validation Hendrik Blockeel, Jan Struyf

Estimating a Kernel Fisher Discriminant in the Presence of Label Noise Neil D. Lawrence, Bernhard Schölkopf

Evolutionary Search, Stochastic Policies with Memory, and Reinforcement Learning with Hidden State Matthew R. Glickman, Katia P. Sycara

Expectation Maximization for Weakly Labeled Data Yuri A. Ivanov, Bruce Blumberg, Alex Pentland

Exploration Control in Reinforcement Learning Using Optimistic Model Selection Jeremy L. Wyatt

Feature Construction with Version Spaces for Biochemical Applications Stefan Kramer, Luc De Raedt

Feature Selection for High-Dimensional Genomic Microarray Data Eric P. Xing, Michael I. Jordan, Richard M. Karp

Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection Sanmay Das

Friend-or-Foe Q-Learning in General-Sum Games Michael L. Littman

General Loss Bounds for Universal Sequence Prediction Marcus Hutter

Hypertext Categorization Using Hyperlink Patterns and Meta Data Rayid Ghani, Seán Slattery, Yiming Yang

Improving Probabilistic Grammatical Inference Core Algorithms with Post-Processing Techniques Franck Thollard

Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems Tobias Scheffer, Stefan Wrobel

Inducing Partially-Defined Instances with Evolutionary Algorithms Xavier Llorà, Josep Maria Garrell i Guiu

Latent Semantic Kernels Nello Cristianini, John Shawe-Taylor, Huma Lodhi

Learnability of Augmented Naive Bayes in Nonimal Domains Huajie Zhang, Charles X. Ling

Learning an Agent's Utility Function by Observing Behavior Urszula Chajewska, Daphne Koller, Dirk Ormoneit

Learning Embedded Maps of Markov Processes Yaakov Engel, Shie Mannor

Learning from Labeled and Unlabeled Data Using Graph Mincuts Avrim Blum, Shuchi Chawla

Learning Probabilistic Models of Relational Structure Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar

Learning to Generate Fast Signal Processing Implementations Bryan Singer, Manuela M. Veloso

Learning to Select Good Title Words: An New Approach Based on Reverse Information Retrieval Rong Jin, Alexander G. Hauptmann

Learning with the Set Covering Machine Mario Marchand, John Shawe-Taylor

Lyapunov-Constrained Action Sets for Reinforcement Learning Theodore J. Perkins, Andrew G. Barto

Mixtures of Rectangles: Interpretable Soft Clustering Dan Pelleg, Andrew W. Moore

Multiple Instance Regression Soumya Ray, David Page

Multiple-Instance Learning of Real-Valued Data Robert A. Amar, Daniel R. Dooly, Sally A. Goldman, Qi Zhang

Obtaining Calibrated Probability Estimates from Decision Trees and Naive Bayesian Classifiers Bianca Zadrozny, Charles Elkan

Off-Policy Temporal Difference Learning with Function Approximation Doina Precup, Richard S. Sutton, Sanjoy Dasgupta

On No-Regret Learning, Fictitious Play, and Nash Equilibrium Amir Jafari, Amy Greenwald, David Gondek, Gunes Ercal

Pairwise Comparison of Hypotheses in Evolutionary Learning Krzysztof Krawiec

Reinforcement Learning in Dynamic Environments Using Instantiated Information Marco A. Wiering

Reinforcement Learning with Bounded Risk Peter Geibel

Relevance Feedback Using Support Vector Machines Harris Drucker, Behzad Shahraray, David C. Gibbon

Repairing Faulty Mixture Models Using Density Estimation Peter Sand, Andrew W. Moore

Ridge Regression Confidence Machine Ilia Nouretdinov, Thomas Melluish, Volodya Vovk

Round Robin Rule Learning Johannes Fürnkranz

Scaling Reinforcement Learning Toward RoboCup Soccer Peter Stone, Richard S. Sutton

Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation Gregory Shakhnarovich, Ran El-Yaniv, Yoram Baram

Some Greedy Algorithms for Sparse Nonlinear Regression Prasanth B. Nair, Arindam Choudhury, Andy J. Keane

Some Sparse Approximation Bounds for Regression Problems Tong Zhang

Some Theoretical Aspects of Boosting in the Presence of Noisy Data Wenxin Jiang

Structured Prioritised Sweeping Richard Dearden

Symmetry in Markov Decision Processes and Its Implications for Single Agent and Multiagent Learning Martin Zinkevich, Tucker R. Balch

Toward Optimal Active Learning Through Sampling Estimation of Error Reduction Nicholas Roy, Andrew McCallum

Unsupervised Sequence Segmentation by a Mixture of Switching Variable Memory Markov Sources Yevgeny Seldin, Gill Bejerano, Naftali Tishby

Using EM to Learn 3D Models of Indoor Environments with Mobile Robots Yufeng Liu, Rosemary Emery, Deepayan Chakrabarti, Wolfram Burgard, Sebastian Thrun

Using the Genetic Algorithm to Reduce the Size of a Nearest-Neighbor Classifier and to Select Relevant Attributes Antonin Rozsypal, Miroslav Kubat

Visual Development and the Acquisition of Binocular Disparity Sensitivities Melissa Dominguez, Robert A. Jacobs

WBCsvm: Weighted Bayesian Classification Based on Support Vector Machines Thomas Gärtner, Peter A. Flach