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20th ICML 2003: Washington, DC, USA
- Tom Fawcett, Nina Mishra:
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, Ramón 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 - Fábio 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 D. 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 M. 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
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 D. 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, Alexander 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. M. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger:
Tackling the Poor Assumptions of Naive Bayes Text Classifiers. 616-623 - Matthew Richardson, Pedro M. 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 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. M. 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 Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, Peter Stone:
Learning Predictive State Representations. 712-719 - Nathan Srebro, Tommi S. 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, Alexander J. Smola, M. Narasimha Murty:
SimpleSVM. 760-767 - Vladimir Vovk, Ilia Nouretdinov, Alex 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 G. 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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