ICML 2002

87 papers

A Boosted Maximum Entropy Model for Learning Text Chunking Seong-Bae Park, Byoung-Tak Zhang

A Fast Dual Algorithm for Kernel Logistic Regression S. Sathiya Keerthi, Kaibo Duan, Shirish K. Shevade, Aun Neow Poo

A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation Artur Merke, Ralf Schoknecht

A New Statistical Approach to Personal Name Extraction Zheng Chen, Liu Wenyin, Feng Zhang

A Unified Decomposition of Ensemble Loss for Predicting Ensemble Performance Michael Goebel, Patricia J. Riddle, Mike Barley

Action Refinement in Reinforcement Learning by Probability Smoothing Thomas G. Dietterich, Dídac Busquets, Ramón López de Mántaras, Carles Sierra

Active + Semi-Supervised Learning = Robust Multi-View Learning Ion Muslea, Steven Minton, Craig A. Knoblock

Adaptive View Validation: A First Step Towards Automatic View Detection Ion Muslea, Steven Minton, Craig A. Knoblock

Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs Carlos Guestrin, Relu Patrascu, Dale Schuurmans

An Alternate Objective Function for Markovian Fields Sham M. Kakade, Yee Whye Teh, Sam T. Roweis

An Analysis of Functional Trees João Gama

An Epsilon-Optimal Grid-Based Algorithm for Partially Observable Markov Decision Processes Blai Bonet

Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry Dennis DeCoste

Approximately Optimal Approximate Reinforcement Learning Sham M. Kakade, John Langford

Classification Value Grouping Colin K. M. Ho

Combining Labeled and Unlabeled Data for MultiClass Text Categorization Rayid Ghani

Combining Trainig Set and Test Set Bounds John Langford

Competitive Analysis of the Explore/Exploit Tradeoff John Langford, Martin Zinkevich, Sham M. Kakade

Constraint-Based Learning of Long Relational Concepts Jacques Ales Bianchetti, Céline Rouveirol, Michèle Sebag

Content-Based Image Retrieval Using Multiple-Instance Learning Qi Zhang, Sally A. Goldman, Wei Yu, Jason E. Fritts

Coordinated Reinforcement Learning Carlos Guestrin, Michail G. Lagoudakis, Ronald Parr

Cranking: Combining Rankings Using Conditional Probability Models on Permutations Guy Lebanon, John D. Lafferty

Descriptive Induction Through Subgroup Discovery: A Case Study in a Medical Domain Dragan Gamberger, Nada Lavrac

Diffusion Kernels on Graphs and Other Discrete Input Spaces Risi Kondor, John D. Lafferty

Discovering Hierarchy in Reinforcement Learning with HEXQ Bernhard Hengst

Discriminative Feature Selection via Multiclass Variable Memory Markov Model Noam Slonim, Gill Bejerano, Shai Fine, Naftali Tishby

Exact Model Averaging with Naive Bayesian Classifiers Denver Dash, Gregory F. Cooper

Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data Joseph Bockhorst, Mark Craven

Fast Minimum Training Error Discretization Tapio Elomaa, Juho Rousu

Feature Selection with Selective Sampling Huan Liu, Hiroshi Motoda, Lei Yu

Feature Subset Selection and Inductive Logic Programming Érick Alphonse, Stan Matwin

Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction Fumio Takechi, Einoshin Suzuki

From Instance-Level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering Dan Klein, Sepandar D. Kamvar, Christopher D. Manning

Graph-Based Relational Concept Learning Jesus A. Gonzalez, Lawrence B. Holder, Diane J. Cook

Hierarchically Optimal Average Reward Reinforcement Learning Mohammad Ghavamzadeh, Sridhar Mahadevan

How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness Alexander K. Seewald

IEMS - The Intelligent Email Sorter Elisabeth Crawford, Judy Kay, Eric McCreath

Incorporating Prior Knowledge into Boosting Robert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra K. Gupta

Inducing Process Models from Continuous Data Pat Langley, Javier Nicolás Sánchez, Ljupco Todorovski, Saso Dzeroski

Integrating Experimentation and Guidance in Relational Reinforcement Learning Kurt Driessens, Saso Dzeroski

Interpreting and Extending Classical Agglomerative Clustering Algorithms Using a Model-Based Approach Sepandar D. Kamvar, Dan Klein, Christopher D. Manning

Investigating the Maximum Likelihood Alternative to TD(lambda) Fletcher Lu, Relu Patrascu, Dale Schuurmans

Is Combining Classifiers Better than Selecting the Best One Saso Dzeroski, Bernard Zenko

Issues in Classifier Evaluation Using Optimal Cost Curves Kai Ming Ting

Kernels for Semi-Structured Data Hisashi Kashima, Teruo Koyanagi

Learning Decision Rules by Randomized Iterative Local Search Michael Chisholm, Prasad Tadepalli

Learning Decision Trees Using the Area Under the ROC Curve César Ferri, Peter A. Flach, José Hernández-Orallo

Learning from Scarce Experience Leonid Peshkin, Christian R. Shelton

Learning K-Reversible Context-Free Grammars from Positive Structural Examples Tim Oates, Devina Desai, Vinay Bhat

Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous World Anand Panangadan, Michael G. Dyer

Learning the Kernel Matrix with Semi-Definite Programming Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan

Learning to Fly by Controlling Dynamic Instabilities David Stirling

Learning to Share Distributed Probabilistic Beliefs Christopher Leckie, Kotagiri Ramamohanarao

Learning Word Normalization Using Word Suffix and Context from Unlabeled Data Dunja Mladenic

Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning David D. Jensen, Jennifer Neville

Markov Chain Monte Carlo Sampling Using Direct Search Optimization Malcolm J. A. Strens, Mark Bernhardt, Nicholas Everett

Mining Both Positive and Negative Association Rules Xindong Wu, Chengqi Zhang, Shichao Zhang

MMIHMM: Maximum Mutual Information Hidden Markov Models Nuria Oliver, Ashutosh Garg

Model-Based Hierarchical Average-Reward Reinforcement Learning Sandeep Seri, Prasad Tadepalli

Modeling Auction Price Uncertainty Using Boosting-Based Conditional Density Estimation Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik

Modeling for Optimal Probability Prediction Yong Wang, Ian H. Witten

Multi-Instance Kernels Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alexander J. Smola

Non-Disjoint Discretization for Naive-Bayes Classifiers Ying Yang, Geoffrey I. Webb

On Generalization Bounds, Projection Profile, and Margin Distribution Ashutosh Garg, Sariel Har-Peled, Dan Roth

On the Existence of Fixed Points for Q-Learning and Sarsa in Partially Observable Domains Theodore J. Perkins, Mark D. Pendrith

Partially Supervised Classification of Text Documents Bing Liu, Wee Sun Lee, Philip S. Yu, Xiaoli Li

PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning Marc Pickett, Andrew G. Barto

Pruning Improves Heuristic Search for Cost-Sensitive Learning Valentina Bayer Zubek, Thomas G. Dietterich

Qualitative Reverse Engineering Dorian Suc, Ivan Bratko

Randomized Variable Elimination David J. Stracuzzi, Paul E. Utgoff

Refining the Wrapper Approach - Smoothed Error Estimates for Feature Selection Loo-Nin Teow, Haifeng Liu, Hwee Tou Ng, Eric Yap

Reinforcement Learning and Shaping: Encouraging Intended Behaviors Adam Laud, Gerald DeJong

Representational Upper Bounds of Bayesian Networks Huajie Zhang, Charles X. Ling

Scalable Internal-State Policy-Gradient Methods for POMDPs Douglas Aberdeen, Jonathan Baxter

Semi-Supervised Clustering by Seeding Sugato Basu, Arindam Banerjee, Raymond J. Mooney

Separating Skills from Preference: Using Learning to Program by Reward Daniel G. Shapiro, Pat Langley

Sparse Bayesian Learning for Regression and Classification Using Markov Chain Monte Carlo Shien-Shin Tham, Arnaud Doucet, Kotagiri Ramamohanarao

Statistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond Tong Zhang

Stock Trading System Using Reinforcement Learning with Cooperative Agents Jangmin O, Jae Won Lee, Byoung-Tak Zhang

Sufficient Dimensionality Reduction - A Novel Analysis Method Amir Globerson, Naftali Tishby

Syllables and Other String Kernel Extensions Craig Saunders, Hauke Tschach, John Shawe-Taylor

The Perceptron Algorithm with Uneven Margins Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz S. Kandola

Towards "Large Margin" Speech Recognizers by Boosting and Discriminative Training Carsten Meyer, Peter Beyerlein

Transformation-Based Regression Björn Bringmann, Stefan Kramer, Friedrich Neubarth, Hannes Pirker, Gerhard Widmer

Univariate Polynomial Inference by Monte Carlo Message Length Approximation Leigh J. Fitzgibbon, David L. Dowe, Lloyd Allison

Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies Malcolm R. K. Ryan

Using Unlabelled Data for Text Classification Through Addition of Cluster Parameters Bhavani Raskutti, Herman L. Ferrá, Adam Kowalczyk