ICML 2003

117 papers

A Faster Iterative Scaling Algorithm for Conditional Exponential Model Rong Jin, Rong Yan, Jian Zhang, Alexander G. Hauptmann

A Kernel Between Sets of Vectors Risi Kondor, Tony Jebara

A Loss Function Analysis for Classification Methods in Text Categorization Fan Li, Yiming Yang

Action Elimination and Stopping Conditions for Reinforcement Learning Eyal Even-Dar, Shie Mannor, Yishay Mansour

Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning Gang Wu, Edward Y. Chang

Adaptive Overrelaxed Bound Optimization Methods Ruslan Salakhutdinov, Sam T. Roweis

An Analysis of Rule Evaluation Metrics Johannes Fürnkranz, Peter A. Flach

An Evaluation on Feature Selection for Text Clustering Tao Liu, Shengping Liu, Zheng Chen, Wei-Ying Ma

Avoiding Bias When Aggregating Relational Data with Degree Disparity David D. Jensen, Jennifer Neville, Michael Hay

AWESOME: A General Multiagent Learning Algorithm That Converges in Self-Play and Learns a Best Response Against Stationary Opponents Vincent Conitzer, Tuomas Sandholm

Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning Yaakov Engel, Shie Mannor, Ron Meir

Bayesian Network Anomaly Pattern Detection for Disease Outbreaks Weng-Keen Wong, Andrew W. Moore, Gregory F. Cooper, Michael M. Wagner

BL-WoLF: A Framework for Loss-Bounded Learnability in Zero-Sum Games Vincent Conitzer, Tuomas Sandholm

Boosting Lazy Decision Trees Xiaoli Zhang Fern, Carla E. Brodley

Characteristics of Long-Term Learning in Soar and Its Application to the Utility Problem William G. Kennedy, Kenneth A. De Jong

Choosing Between Two Learning Algorithms Based on Calibrated Tests Remco R. Bouckaert

Classification of Text Documents Based on Minimum System Entropy Raghu Krishnapuram, Krishna Prasad Chitrapura, Sachindra Joshi

Combining TD-Learning with Cascade-Correlation Networks François Rivest, Doina Precup

Correlated Q-Learning Amy Greenwald, Keith Hall

Cross-Entropy Directed Embedding of Network Data Takeshi Yamada, Kazumi Saito, Naonori Ueda

Decision Tree with Better Ranking Charles X. Ling, Robert J. Yan

Decision-Tree Induction from Time-Series Data Based on a Standard-Example Split Test Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi

Design for an Optimal Probe Michael O. Duff

Diffusion Approximation for Bayesian Markov Chains Michael O. Duff

Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers Aldebaro Klautau, Nikola Jevtic, Alon Orlitsky

DISTILL: Learning Domain-Specific Planners by Example Elly Winner, Manuela M. Veloso

Eliminating Class Noise in Large Datasets Xingquan Zhu, Xindong Wu, Qijun Chen

Error Bounds for Approximate Policy Iteration Rémi Munos

Evolutionary MCMC Sampling and Optimization in Discrete Spaces Malcolm J. A. Strens

Evolving Strategies for Focused Web Crawling Judy Johnson, Kostas Tsioutsiouliklis, C. Lee Giles

Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning Yi Zhang, Wei Xu, James P. Callan

Exploration in Metric State Spaces Sham M. Kakade, Michael J. Kearns, John Langford

Fast Query-Optimized Kernel Machine Classification via Incremental Approximate Nearest Support Vectors Dennis DeCoste, Dominic Mazzoni

Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution Lei Yu, Huan Liu

Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries Jeremy Kubica, Andrew W. Moore, David Cohn, Jeff G. Schneider

Flexible Mixture Model for Collaborative Filtering Luo Si, Rong Jin

Goal-Directed Learning to Fly Andrew Isaac, Claude Sammut

Hidden Markov Support Vector Machines Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofmann

Hierarchical Latent Knowledge Analysis for Co-Occurrence Data Hiroshi Mamitsuka

Hierarchical Policy Gradient Algorithms Mohammad Ghavamzadeh, Sridhar Mahadevan

Identifying Predictive Structures in Relational Data Using Multiple Instance Learning Amy McGovern, David D. Jensen

Improving Accuracy and Cost of Two-Class and Multi-Class Probabilistic Classifiers Using ROC Curves Nicolas Lachiche, Peter A. Flach

Incorporating Diversity in Active Learning with Support Vector Machines Klaus Brinker

Informative Discriminant Analysis Samuel Kaski, Jaakko Peltonen

Isometric Embedding and Continuum ISOMAP Hongyuan Zha, Zhenyue Zhang

Justification-Based Multiagent Learning Santiago Ontañón, Enric Plaza

Kernel PLS-SVC for Linear and Nonlinear Classification Roman Rosipal, Leonard J. Trejo, Bryan Matthews

Learning Distance Functions Using Equivalence Relations Aharon Bar-Hillel, Tomer Hertz, Noam Shental, Daphna Weinshall

Learning from Attribute Value Taxonomies and Partially Specified Instances Jun Zhang, Vasant G. Honavar

Learning Logic Programs for Layout Analysis Correction Margherita Berardi, Michelangelo Ceci, Floriana Esposito, Donato Malerba

Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation Zhihua Zhang

Learning Mixture Models with the Latent Maximum Entropy Principle Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao

Learning on the Test Data: Leveraging Unseen Features Benjamin Taskar, Ming Fai Wong, Daphne Koller

Learning Predictive State Representations Satinder Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, Peter Stone

Learning to Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining Jeff L. Stimpson, Michael A. Goodrich

Learning with Idealized Kernels James T. Kwok, Ivor W. Tsang

Learning with Knowledge from Multiple Experts Matthew Richardson, Pedro M. Domingos

Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression Wee Sun Lee, Bing Liu

Linear Programming Boosting for Uneven Datasets Jure Leskovec, John Shawe-Taylor

Link-Based Classification Qing Lu, Lise Getoor

Low Bias Bagged Support Vector Machines Giorgio Valentini, Thomas G. Dietterich

Machine Learning with Hyperkernels Cheng Soon Ong, Alexander J. Smola

Margin Distribution and Learning Ashutosh Garg, Dan Roth

Marginalized Kernels Between Labeled Graphs Hisashi Kashima, Koji Tsuda, Akihiro Inokuchi

Mixtures of Conditional Maximum Entropy Models Dmitry Pavlov, Alexandrin Popescul, David M. Pennock, Lyle H. Ungar

Model-Based Policy Gradient Reinforcement Learning Xin Wang, Thomas G. Dietterich

Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization Jian Zhang, Rong Jin, Yiming Yang, Alexander G. Hauptmann

Multi-Objective Programming in SVMs Jinbo Bi

New Í-Support Vector Machines and Their Sequential Minimal Optimization Xiaoyun Wu, Rohini K. Srihari

On Kernel Methods for Relational Learning Chad M. Cumby, Dan Roth

On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy Data Marc Sebban, Jean-Christophe Janodet

On the Convergence of Boosting Procedures Tong Zhang, Bin Yu

Online Choice of Active Learning Algorithms Yoram Baram, Ran El-Yaniv, Kobi Luz

Online Convex Programming and Generalized Infinitesimal Gradient Ascent Martin Zinkevich

Online Feature Selection Using Grafting Simon Perkins, James Theiler

Online Ranking/Collaborative Filtering Using the Perceptron Algorithm Edward F. Harrington

Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning Andrew W. Moore, Weng-Keen Wong

Optimization with EM and Expectation-Conjugate-Gradient Ruslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahramani

Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic Lian Yan, Robert H. Dodier, Michael Mozer, Richard H. Wolniewicz

Perceptron Based Learning with Example Dependent and Noisy Costs Peter Geibel, Fritz Wysotzki

Planning in the Presence of Cost Functions Controlled by an Adversary H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum

Principled Methods for Advising Reinforcement Learning Agents Eric Wiewiora, Garrison W. Cottrell, Charles Elkan

Probabilistic Classifiers and the Concepts They Recognize Manfred Jaeger

Q-Decomposition for Reinforcement Learning Agents Stuart Russell, Andrew Zimdars

Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach Xiaoli Zhang Fern, Carla E. Brodley

Regression Error Characteristic Curves Jinbo Bi, Kristin P. Bennett

Reinforcement Learning as Classification: Leveraging Modern Classifiers Michail G. Lagoudakis, Ronald Parr

Relational Instance Based Regression for Relational Reinforcement Learning Kurt Driessens, Jan Ramon

Relativized Options: Choosing the Right Transformation Balaraman Ravindran, Andrew G. Barto

Representational Issues in Meta-Learning Alexandros Kalousis, Melanie Hilario

Robust Induction of Process Models from Time-Series Data Pat Langley, Dileep George, Stephen D. Bay, Kazumi Saito

Semi-Supervised Learning of Mixture Models Fábio Gagliardi Cozman, Ira Cohen, Marcelo Cesar Cirelo

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty

SimpleSVM S. V. N. Vishwanathan, Alexander J. Smola, M. Narasimha Murty

Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations Thore Graepel

Stochastic Local Search in K-Term DNF Learning Ulrich Rückert, Stefan Kramer

Tackling the Poor Assumptions of Naive Bayes Text Classifiers Jason D. M. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger

TD(0) Converges Provably Faster than the Residual Gradient Algorithm Ralf Schoknecht, Artur Merke

Testing Exchangeability On-Line Vladimir Vovk, Ilia Nouretdinov, Alex Gammerman

Text Bundling: Statistics Based Data-Reduction Lawrence Shih, Jason D. M. Rennie, Yu-Han Chang, David R. Karger

Text Classification Using Stochastic Keyword Generation Cong Li, Ji-Rong Wen, Hang Li

The Cross Entropy Method for Fast Policy Search Shie Mannor, Reuven Y. Rubinstein, Yohai Gat

The Geometry of ROC Space: Understanding Machine Learning Metrics Through ROC Isometrics Peter A. Flach

The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping Adam Laud, Gerald DeJong

The Pre-Image Problem in Kernel Methods James T. Kwok, Ivor W. Tsang

The Set Covering Machine with Data-Dependent Half-Spaces Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova

The Significance of Temporal-Difference Learning in Self-Play Training TD-Rummy Versus EVO-Rummy Clifford Kotnik, Jugal K. Kalita

The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods Gavin Brown, Jeremy L. Wyatt

Tractable Bayesian Learning of Tree Augmented Naive Bayes Models Jesús Cerquides, Ramón López de Mántaras

Transductive Learning via Spectral Graph Partitioning Thorsten Joachims

Unsupervised Learning with Permuted Data Sergey Kirshner, Sridevi Parise, Padhraic Smyth

Using Linear-Threshold Algorithms to Combine Multi-Class Sub-Experts Chris Mesterharm

Using the Triangle Inequality to Accelerate K-Means Charles Elkan

Utilizing Domain Knowledge in Neuroevolution James Fan, Raymond Lau, Risto Miikkulainen

Visual Learning by Evolutionary Feature Synthesis Krzysztof Krawiec, Bir Bhanu

Weighted Low-Rank Approximations Nathan Srebro, Tommi S. Jaakkola

Weighted Order Statistic Classifiers with Large Rank-Order Margin Reid B. Porter, Damian Eads, Don R. Hush, James Theiler