20. ICML 2003:
Washington, DC, USA
Tom Fawcett, Nina Mishra (Eds.):
Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA.
AAAI Press 2003, ISBN 1-57735-189-4 
- Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofmann:
Hidden Markov Support Vector Machines.
3-10
- Aharon Bar-Hillel, Tomer Hertz, Noam Shental, Daphna Weinshall:
Learning Distance Functions using Equivalence Relations.
11-18
- Yoram Baram, Ran El-Yaniv, Kobi Luz:
Online Choice of Active Learning Algorithms.
19-26
- Margherita Berardi, Michelangelo Ceci, Floriana Esposito, Donato Malerba:
Learning Logic Programs for Layout Analysis Correction.
27-34
- Jinbo Bi:
Multi-Objective Programming in SVMs.
35-42
- Jinbo Bi, Kristin P. Bennett:
Regression Error Characteristic Curves.
43-50
- Remco R. Bouckaert:
Choosing Between Two Learning Algorithms Based on Calibrated Tests.
51-58
- Klaus Brinker:
Incorporating Diversity in Active Learning with Support Vector Machines.
59-66
- Gavin Brown, Jeremy L. Wyatt:
The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods.
67-74
- Jesús Cerquides, Ramon López de Mántaras:
Tractable Bayesian Learning of Tree Augmented Naive Bayes Models.
75-82
- Vincent Conitzer, Tuomas Sandholm:
AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents.
83-90
- Vincent Conitzer, Tuomas Sandholm:
BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum Games.
91-98
- Fabio Gagliardi Cozman, Ira Cohen, Marcelo Cesar Cirelo:
Semi-Supervised Learning of Mixture Models.
99-106
- Chad M. Cumby, Dan Roth:
On Kernel Methods for Relational Learning.
107-114
- Dennis DeCoste, Dominic Mazzoni:
Fast Query-Optimized Kernel Machine Classification Via Incremental Approximate Nearest Support Vectors.
115-122
- Kurt Driessens, Jan Ramon:
Relational Instance Based Regression for Relational Reinforcement Learning.
123-130
- Michael O. Duff:
Design for an Optimal Probe.
131-138
- Michael O. Duff:
Diffusion Approximation for Bayesian Markov Chains.
139-146
- Charles Elkan:
Using the Triangle Inequality to Accelerate k-Means.
147-153
- Yaakov Engel, Shie Mannor, Ron Meir:
Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning.
154-161
- Eyal Even-Dar, Shie Mannor, Yishay Mansour:
Action Elimination and Stopping Conditions for Reinforcement Learning.
162-169
- James Fan, Raymond Lau, Risto Miikkulainen:
Utilizing Domain Knowledge in Neuroevolution.
170-177
- Xiaoli Zhang Fern, Carla E. Brodley:
Boosting Lazy Decision Trees.
178-185
- Xiaoli Zhang Fern, Carla E. Brodley:
Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach.
186-193
- Peter A. Flach:
The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics.
194-201
- Johannes Fürnkranz, Peter A. Flach:
An Analysis of Rule Evaluation Metrics.
202-209
- Ashutosh Garg, Dan Roth:
Margin Distribution and Learning.
210-217
- Peter Geibel, Fritz Wysotzki:
Perceptron Based Learning with Example Dependent and Noisy Costs.
218-225
- Mohammad Ghavamzadeh, Sridhar Mahadevan:
Hierarchical Policy Gradient Algorithms.
226-233
- Thore Graepel:
Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations.
234-241
- Amy Greenwald, Keith Hall:
Correlated Q-Learning.
242-249
- Edward F. Harrington:
Online Ranking/Collaborative Filtering Using the Perceptron Algorithm.
250-257
- Andrew Isaac, Claude Sammut:
Goal-directed Learning to Fly.
258-265
- Manfred Jaeger:
Probabilistic Classifiers and the Concepts They Recognize.
266-273
- David Jensen, Jennifer Neville, Michael Hay:
Avoiding Bias when Aggregating Relational Data with Degree Disparity.
274-281
- Rong Jin, Rong Yan, Jian Zhang, Alexander G. Hauptmann:
A Faster Iterative Scaling Algorithm for Conditional Exponential Model.
282-289
- Thorsten Joachims:
Transductive Learning via Spectral Graph Partitioning.
290-297
- Judy Johnson, Kostas Tsioutsiouliklis, C. Lee Giles:
Evolving Strategies for Focused Web Crawling.
298-305
- Sham Kakade, Michael J. Kearns, John Langford:
Exploration in Metric State Spaces.
306-312
- Alexandros Kalousis, Melanie Hilario:
Representational Issues in Meta-Learning.
313-320
- Hisashi Kashima, Koji Tsuda, Akihiro Inokuchi:
Marginalized Kernels Between Labeled Graphs.
321-328
- Samuel Kaski, Jaakko Peltonen:
Informative Discriminant Analysis.
329-336
- William G. Kennedy, Kenneth A. De Jong:
Characteristics of Long-term Learning in Soar and its Application to the Utility Problem.
337-344
- Sergey Kirshner, Sridevi Parise, Padhraic Smyth:
Unsupervised Learning with Permuted Data.
345-352
- Aldebaro Klautau, Nikola Jevtic, Alon Orlitsky:
Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers.
353-360
- Risi Kondor, Tony Jebara:
A Kernel Between Sets of Vectors.
361-368
- Clifford Kotnik, Jugal K. Kalita:
The Significance of Temporal-Difference Learning in Self-Play Training TD-Rummy versus EVO-rummy.
369-375
- Krzysztof Krawiec, Bir Bhanu:
Visual Learning by Evolutionary Feature Synthesis.
376-383
- Raghu Krishnapuram, Krishna Prasad Chitrapura, Sachindra Joshi:
Classification of Text Documents Based on Minimum System Entropy.
384-391
- Jeremy Kubica, Andrew W. Moore, David Cohn, Jeff G. Schneider:
Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries.
392-399
- James T. Kwok, Ivor W. Tsang:
Learning with Idealized Kernels.
400-407
- James T. Kwok, Ivor W. Tsang:
The Pre-Image Problem in Kernel Methods.
408-415
- Nicolas Lachiche, Peter A. Flach:
Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves.
416-423
- Michail G. Lagoudakis, Ronald Parr:
Reinforcement Learning as Classification: Leveraging Modern Classifiers.
424-431
- Pat Langley, Dileep George, Stephen D. Bay, Kazumi Saito:
Robust Induction of Process Models from Time-Series Data.
432-439
- Adam Laud, Gerald DeJong:
The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping.
440-447
- Wee Sun Lee, Bing Liu:
Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression.
448-455
- Jure Leskovec, John Shawe-Taylor:
Linear Programming Boosting for Uneven Datasets.
456-463
- Cong Li, Ji-Rong Wen, Hang Li:
Text Classification Using Stochastic Keyword Generation.
464-471
- Fan Li, Yiming Yang:
A Loss Function Analysis for Classification Methods in Text Categorization.
472-479
- Charles X. Ling, Robert J. Yan:
Decision Tree with Better Ranking.
480-487
- Tao Liu, Shengping Liu, Zheng Chen, Wei-Ying Ma:
An Evaluation on Feature Selection for Text Clustering.
488-495
- Qing Lu, Lise Getoor:
Link-based Classification.
496-503
- Hiroshi Mamitsuka:
Hierarchical Latent Knowledge Analysis for Co-occurrence Data.
504-511
- Shie Mannor, Reuven Y. Rubinstein, Yohai Gat:
The Cross Entropy Method for Fast Policy Search.
512-519
- Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova:
The Set Covering Machine with Data-Dependent Half-Spaces.
520-527
- Amy McGovern, David Jensen:
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning.
528-535
- H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum:
Planning in the Presence of Cost Functions Controlled by an Adversary.
536-543
- Chris Mesterharm:
Using Linear-threshold Algorithms to Combine Multi-class Sub-experts.
544-551
- Andrew W. Moore, Weng-Keen Wong:
Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning.
552-559
- Rémi Munos:
Error Bounds for Approximate Policy Iteration.
560-567
- Cheng Soon Ong, Alex J. Smola:
Machine Learning with Hyperkernels.
568-575
- Santiago Ontañón, Enric Plaza:
Justification-based Multiagent Learning.
576-583
- Dmitry Pavlov, Alexandrin Popescul, David M. Pennock, Lyle H. Ungar:
Mixtures of Conditional Maximum Entropy Models.
584-591
- Simon Perkins, James Theiler:
Online Feature Selection using Grafting.
592-599
- Reid B. Porter, Damian Eads, Don R. Hush, James Theiler:
Weighted Order Statistic Classifiers with Large Rank-Order Margin.
600-607
- Balaraman Ravindran, Andrew G. Barto:
Relativized Options: Choosing the Right Transformation.
608-615
- Jason D. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger:
Tackling the Poor Assumptions of Naive Bayes Text Classifiers.
616-623
- Matthew Richardson, Pedro Domingos:
Learning with Knowledge from Multiple Experts.
624-631
- François Rivest, Doina Precup:
Combining TD-learning with Cascade-correlation Networks.
632-639
- Roman Rosipal, Leonard J. Trejo, Bryan Matthews:
Kernel PLS-SVC for Linear and Nonlinear Classification.
640-647
- Ulrich Rückert, Stefan Kramer:
Stochastic Local Search in k-Term DNF Learning.
648-655
- Stuart J. Russell, Andrew Zimdars:
Q-Decomposition for Reinforcement Learning Agents.
656-663
- Ruslan Salakhutdinov, Sam T. Roweis:
Adaptive Overrelaxed Bound Optimization Methods.
664-671
- Ruslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahramani:
Optimization with EM and Expectation-Conjugate-Gradient.
672-679
- Ralf Schoknecht, Artur Merke:
TD(0) Converges Provably Faster than the Residual Gradient Algorithm.
680-687
- Marc Sebban, Jean-Christophe Janodet:
On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy Data.
688-695
- Lawrence Shih, Jason D. Rennie, Yu-Han Chang, David R. Karger:
Text Bundling: Statistics Based Data-Reduction.
696-703
- Luo Si, Rong Jin:
Flexible Mixture Model for Collaborative Filtering.
704-711
- Satinder P. Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, Peter Stone:
Learning Predictive State Representations.
712-719
- Nathan Srebro, Tommi Jaakkola:
Weighted Low-Rank Approximations.
720-727
- Jeff L. Stimpson, Michael A. Goodrich:
Learning To Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining.
728-735
- Malcolm J. A. Strens:
Evolutionary MCMC Sampling and Optimization in Discrete Spaces.
736-743
- Benjamin Taskar, Ming Fai Wong, Daphne Koller:
Learning on the Test Data: Leveraging Unseen Features.
744-751
- Giorgio Valentini, Thomas G. Dietterich:
Low Bias Bagged Support Vector Machines.
752-759
- S. V. N. Vishwanathan, Alex J. Smola, M. Narasimha Murty:
SimpleSVM.
760-767
- Vladimir Vovk, Ilia Nouretdinov, Alexander Gammerman:
Testing Exchangeability On-Line.
768-775
- Xin Wang, Thomas G. Dietterich:
Model-based Policy Gradient Reinforcement Learning.
776-783
- Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao:
Learning Mixture Models with the Latent Maximum Entropy Principle.
784-791
- Eric Wiewiora, Garrison W. Cottrell, Charles Elkan:
Principled Methods for Advising Reinforcement Learning Agents.
792-799
- Elly Winner, Manuela M. Veloso:
DISTILL: Learning Domain-Specific Planners by Example.
800-807
- Weng-Keen Wong, Andrew W. Moore, Gregory F. Cooper, Michael M. Wagner:
Bayesian Network Anomaly Pattern Detection for Disease Outbreaks.
808-815
- Gang Wu, Edward Y. Chang:
Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning.
816-823
- Xiaoyun Wu, Rohini K. Srihari:
New í-Support Vector Machines and their Sequential Minimal Optimization.
824-831
- Takeshi Yamada, Kazumi Saito, Naonori Ueda:
Cross-Entropy Directed Embedding of Network Data.
832-839
- Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi:
Decision-tree Induction from Time-series Data Based on a Standard-example Split Test.
840-847
- Lian Yan, Robert H. Dodier, Michael Mozer, Richard H. Wolniewicz:
Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic.
848-855
- Lei Yu, Huan Liu:
Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution.
856-863
- Hongyuan Zha, Zhenyue Zhang:
Isometric Embedding and Continuum ISOMAP.
864-871
- Zhihua Zhang:
Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation.
872-879
- Jun Zhang, Vasant Honavar:
Learning from Attribute Value Taxonomies and Partially Specified Instances.
880-887
- Jian Zhang, Rong Jin, Yiming Yang, Alexander G. Hauptmann:
Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization.
888-895
- Yi Zhang, Wei Xu, James P. Callan:
Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning.
896-903
- Tong Zhang, Bin Yu:
On the Convergence of Boosting Procedures.
904-911
- Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty:
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions.
912-919
- Xingquan Zhu, Xindong Wu, Qijun Chen:
Eliminating Class Noise in Large Datasets.
920-927
- Martin Zinkevich:
Online Convex Programming and Generalized Infinitesimal Gradient Ascent.
928-936
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