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Home / Proceedings / Proceedings of the Twentieth International Conference on Machine Learning /

Book One

Contents

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

    Zhihua Zhang

    PDF
  • Learning from Attribute Value Taxonomies and Partially Specified Instances

    Jun Zhang and Vasant Honavar

    PDF
  • Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization

    Jian Zhang, Rong Jin, Yiming Yang, and Alex G. Hauptmann

    PDF
  • Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning

    Yi Zhang, Wei Xu, and Jamie Callan

    PDF
  • On the Convergence of Boosting Procedures

    Tong Zhang and Bin Yu

    PDF
  • Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
    PDF
  • Eliminating Class Noise in Large Datasets

    Xingquan Zhu, Xindong Wu, and Qijun Chen

    PDF
  • Online Convex Programming and Generalized Infinitesimal Gradient Ascent

    Martin Zinkevich

    PDF
  • Learning Predictive State Representations

    Satinder Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, and Peter Stone

    PDF
  • Weighted Low-Rank Approximations

    Nathan Srebro and Tommi Jaakkola

    PDF
  • Learning To Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining

    Jeffrey L. Stimpson and Michael A. Goodrich

    PDF
  • Evolutionary MCMC Sampling and Optimization in Discrete Spaces

    Malcolm J. A. Strens

    PDF
  • Learning on the Test Data: Leveraging Unseen Features

    Ben Taskar, Ming Fai Wong, and Daphne Koller

    PDF
  • Low Bias Bagged Support Vector Machines

    Giorgio Valentini and Thomas G. Dietterich

    PDF
  • SimpleSVM

    S. V. N. Vishwanathan, Alexander J. Smola, and M. Narashima Murty

    PDF
  • Testing Exchangeability On-Line

    Vladimir Vovk, Ilia Nouretdinov, and Alex Gammerman

    PDF
  • Model-based Policy Gradient Reinforcement Learning

    Xin Wang and Thomas G. Dietterich

    PDF
  • Learning Mixture Models with the Latent Maximum Entropy Principle

    Shaojun Wang, Dale Schuurmans, Fuchun Peng, and Yunxin Zhao

    PDF
  • Principled Methods for Advising Reinforcement Learning Agents

    Eric Wiewiora, Garrison Cottrell, and Charles Elkan

    PDF
  • DISTILL: Learning Domain-Specific Planners by Example

    Elly Winner and Manuela Veloso

    PDF
  • Bayesian Network Anomaly Pattern Detection for Disease Outbreaks

    Weng-Keen Wong, Andrew Moore, Gregory Cooper, and Michael Wagner

    PDF
  • Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning
    PDF
  • New ν-Support Vector Machines and their Sequential Minimal Optimization

    Xiaoyun Wu and Rohini Srihari

    PDF
  • Cross-Entropy Directed Embedding of Network Data

    Takeshi Yamada, Kazumi Saito, and Naonori Ueda

    PDF
  • Decision-tree Induction from Time-series Data Based on a Standard-example Split Test

    Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, and Katsuhiko Takabayashi

    PDF
  • Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic

    Lian Yan, Robert Dodier, Michael C. Mozer, and Richard Wolniewicz

    PDF
  • Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution

    Lei Yu and Huan Liu

    PDF
  • Isometric Embedding and Continuum ISOMAP

    Hongyuan Zha, and Zhenyue Zhang

    PDF
  • Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning

    Andrew Moore and Weng-Keen Wong

    PDF
  • Error Bounds for Approximate Policy Iteration

    Rémi Munos

    PDF
  • Machine Learning with Hyperkernels

    Cheng Soon Ong and Alexander J. Smola

    PDF
  • Justification-based Multiagent Learning

    Santi Ontañón and Enric Plaza

    PDF
  • Mixtures of Conditional Maximum Entropy Models

    Dmitry Pavlov, Alexandrin Popescul, David M. Pennock, and Lyle H. Ungar

    PDF
  • Online Feature Selection using Grafting

    Simon Perkins and James Theiler

    PDF
  • Weighted Order Statistic Classifiers with Large Rank-Order Margin

    Reid Porter, Damian Eads, Don Hush, and James Theiler

    PDF
  • Relativized Options: Choosing the Right Transformation

    Balaraman Ravindran and Andrew G. Barto

    PDF
  • Tackling the Poor Assumptions of Naive Bayes Text Classifiers

    Jason D. Rennie, Lawrence Shih, Jaime Teevan, and David Karger

    PDF
  • Learning with Knowledge from Multiple Experts

    Matthew Richardson and Pedro Domingos

    PDF
  • Combining td-learning with Cascade-correlation Networks

    François Rivest and Doina Precup

    PDF
  • Kernel PLS-SVC for Linear and Nonlinear Classification

    Roman Rosipal, Leonard J. Trejo, and Bryan Matthews

    PDF
  • Stochastic Local Search in k-Term DNF Learning

    Ulrich Rückert and Stefan Kramer

    PDF
  • Q-Decomposition for Reinforcement Learning Agents

    Stuart Russell and Andrew L. Zimdars

    PDF
  • Adaptive Overrelaxed Bound Optimization Methods

    Ruslan Salakhutdinov and Sam Roweis

    PDF
  • Optimization with EM and Expectation-Conjugate-Gradient

    Ruslan Salakhutdinov, Sam Roweis, and Zoubin Ghahramani

    PDF
  • td(0) Converges Provably Faster than the Residual Gradient Algorithm

    Ralf Schoknecht and Artur Merke

    PDF
  • On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy Data

    Marc Sebban and Jean-Christophe Janodet

    PDF
  • Text Bundling: Statistics Based Data-Reduction

    Lawrence Shih, Jason Rennie, Yu-Han Chang, and David Karger

    PDF
  • Flexible Mixture Model for Collaborative Filtering

    Luo Si and Rong Jin

    PDF
  • Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries

    Jeremy Kubica, Andrew Moore, David Cohn, and Jeff Schneider

    PDF
  • Learning with Idealized Kernels

    James T. Kwok and Ivor W. Tsang

    PDF
  • The Pre-Image Problem in Kernel Methods

    James Kwok and Ivor Tsang

    PDF
  • Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves

    Nicolas Lachiche and Peter Flach

    PDF
  • Reinforcement Learning as Classification: Leveraging Modern Classifiers

    Michail Lagoudakis and Ronald Parr

    PDF
  • Robust Induction of Process Models from Time-Series Data

    Pat Langley, Dileep George, Stephen Bay, and Kazumi Saito

    PDF
  • The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping

    Adam Laud and Gerald DeJong

    PDF
  • Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression

    Wee Sun Lee and Bing Liu

    PDF
  • Linear Programming Boosting for Uneven Datasets

    Jurij Leskovec and John Shawe-Taylor

    PDF
  • Text Classification Using Stochastic Keyword Generation

    Cong Li, Ji-Rong Wen, and Hang Li

    PDF
  • A Loss Function Analysis for Classification Methods in Text Categorization

    Fan Li and Yiming Yang

    PDF
  • Decision Tree with Better Ranking

    Charles X. Ling and Robert J. Yan

    PDF
  • An Evaluation on Feature Selection for Text Clustering

    Tao Liu, Shengping Liu, Zheng Chen, and Wei-Ying Ma

    PDF
  • Link-based Classification

    Qing Lu and Lise Getoor

    PDF
  • Hierarchical Latent Knowledge Analysis for Co-occurrence Data

    Hiroshi Mamitsuka

    PDF
  • The Cross Entropy Method for Fast Policy Search

    Shie Mannor, Reuven Rubinstein, and Yohai Gat

    PDF
  • The Set Covering Machine with Data-Dependent Half-Spaces

    Mario Marchand, Mohak Shah, John Shawe-Taylor, and Marina Sokolova

    PDF
  • Identifying Predictive Structures in Relational Data Using Multiple Instance Learning

    Amy McGovern and David Jensen

    PDF
  • Planning in the Presence of Cost Functions Controlled by an Adversary

    H. Brendan McMahan, Geoffrey J. Gordon, and Avrim Blum

    PDF
  • Using Linear-threshold Algorithms to Combine Multi-class Sub-experts

    Chris Mesterharm

    PDF
  • Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations

    Thore Graepel

    PDF
  • Correlated Q-Learning

    Amy Greenwald and Keith Hall

    PDF
  • Edward F. Harrington
    PDF
  • Goal-directed Learning to Fly

    Andrew Isaac and Claude Sammut

    PDF
  • Probabilistic Classifiers and the Concepts They Recognize

    Manfred Jaeger

    PDF
  • Avoiding Bias when Aggregating Relational Data with Degree Disparity

    David Jensen, Jennifer Neville, and Michael Hay

    PDF
  • Rong Jin, Rong Yan, Jian Zhang, and Alex G. Hauptmann
    PDF
  • Transductive Learning via Spectral Graph Partitioning

    Thorsten Joachims

    PDF
  • Evolving Strategies for Focused Web Crawling

    Judy Johnson, Kostas Tsioutsiouliklis, and C. Lee Giles

    PDF
  • Sham Kakade, Michael Kearns, and John Langford

    Sham Kakade, Michael Kearns, and John Langford

    PDF
  • Representational Issues in Meta-Learning

    Alexandros Kalousis and Melanie Hilario

    PDF
  • Marginalized Kernels Between Labeled Graphs

    Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi

    PDF
  • Informative Discriminant Analysis

    Samuel Kaski and Jaakko Peltonen

    PDF
  • Characteristics of Long-term Learning in Soar and its Application to the Utility Problem

    William G. Kennedy and Kenneth A. De Jong

    PDF
  • Unsupervised Learning with Permuted Data

    Sergey Kirshner, Sridevi Parise, and Padhraic Smyth

    PDF
  • Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers

    Aldebaro Klautau, Nikola Jevtic, and Alon Orlitsky

    PDF
  • A Kernel Between Sets of Vectors

    Risi Kondor and Tony Jebara

    PDF
  • The Significance of Temporal-Difference Learning in Self-Play Training td-Rummy versus EVO-rummy

    Clifford Kotnik and Jugal Kalita

    PDF
  • Visual Learning by Evolutionary Feature Synthesis

    Krzysztof Krawiec and Bir Bhanu

    PDF
  • Classification of Text Documents Based on Minimum System Entropy

    Raghu Krishnapuram, Krishna P. Chitrapura, and Sachindra Joshi

    PDF
  • Tractable Bayesian Learning of Tree Augmented Naive Bayes Models

    Jesüs Cerquides and Ramon López de Màntaras

    PDF
  • AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents

    Vincent Conitzer and Tuomas Sandholm

    PDF
  • BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum Games

    Vincent Conitzer and Tuomas Sandholm

    PDF
  • Semi-Supervised Learning of Mixture Models

    Fabio Gagliardi Cozman, Ira Cohen, and Marcelo Cesar Cirelo

    PDF
  • On Kernel Methods for Relational Learning

    Chad Cumby and Dan Roth

    PDF
  • Fast Query-Optimized Kernel Machine Classification Via Incremental Approximate Nearest Support Vectors

    Dennis DeCoste and Dominic Mazzoni

    PDF
  • Relational Instance Based Regression for Relational Reinforcement Learning

    Kurt Driessens and Jan Ramon

    PDF
  • Design for an Optimal Probe

    Michael Duff

    PDF
  • Diffusion Approximation for Bayesian Markov Chains

    Michael Duff

    PDF
  • Using the Triangle Inequality to Accelerate k-Means

    Charles Elkan

    PDF
  • Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning

    Yaakov Engel, Shie Mannor, and Ron Meir

    PDF
  • Action Elimination and Stopping Conditions for Reinforcement Learning

    Eyal Even-Dar, Shie Mannor, and Yishay Mansour

    PDF
  • Utilizing Domain Knowledge in Neuroevolution

    James Fan, Raymond Lau, and Risto Miikkulainen

    PDF
  • Boosting Lazy Decision Trees

    Xiaoli Zhang Fern and Carla E. Brodley

    PDF
  • Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach

    Xiaoli Zhang Fern and Carla E. Brodley

    PDF
  • The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics

    Peter A. Flach

    PDF
  • An Analysis of Rule Evaluation Metrics

    Johannes Fürnkranz and Peter A. Flach

    PDF
  • Margin Distribution and Learning

    Ashutosh Garg and Dan Roth

    PDF
  • Perceptron Based Learning with Example Dependent and Noisy Costs
    PDF
  • Hierarchical Policy Gradient Algorithms

    Mohammad Ghavamzadeh and Sridhar Mahadevan

    PDF
  • Contents, Proceedings of the International Conference on Machine Learning

    Edited by Tom Fawcett and Nina Mishra

    PDF
  • Preface

    Tom Fawcett and Nina Mishra

    PDF
  • ICML 2003 Organization

    Edited by Tom Fawcett and Nina Mishra

    PDF
  • ICML 2003 Corporate Sponsors

    Tom Fawcett and Nina Mishra

    PDF
  • Hidden Markov Support Vector Machines

    Yasemin Altun, Ioannis Tsochantaridis, and Thomas Hofmann

    PDF
  • Learning Distance Functions using Equivalence Relations

    Aharon Bar-Hillel, Tomer Hertz, Noam Shental, and Daphna Weinshall

    PDF
  • Online Choice of Active Learning Algorithms

    Yoram Baram, Ran El-Yaniv, and Kobi Luz

    PDF
  • Learning Logic Programs for Layout Analysis Correction

    Margherita Berardi, Michelangelo Ceci, Floriana Esposito, and Donato Malerba

    PDF
  • Multi-Objective Programming in SVMs

    Jinbo Bi

    PDF
  • Regression Error Characteristic Curves

    Jinbo Bi and Kristin P. Bennett

    PDF
  • Choosing Between Two Learning Algorithms Based on Calibrated Tests

    Remco R. Bouckaert

    PDF
  • Incorporating Diversity in Active Learning with Support Vector Machines

    Klaus Brinker

    PDF
  • The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods

    Gavin Brown and Jeremy Wyatt

    PDF

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