Table of Contents

• Hidden Markov Support Vector MachinesYasemin Altun, Ioannis Tsochantaridis, Thomas Hofmann. 3-10
• Learning Distance Functions using Equivalence RelationsAharon Bar-Hillel, Tomer Hertz, Noam Shental, Daphna Weinshall. 11-18
• Online Choice of Active Learning AlgorithmsYoram Baram, Ran El-Yaniv, Kobi Luz. 19-26
• Learning Logic Programs for Layout Analysis CorrectionMargherita Berardi, Michelangelo Ceci, Floriana Esposito, Donato Malerba. 27-34
• Multi-Objective Programming in SVMsJinbo Bi. 35-42
• Regression Error Characteristic CurvesJinbo Bi, Kristin P. Bennett. 43-50
• Choosing Between Two Learning Algorithms Based on Calibrated TestsRemco R. Bouckaert. 51-58
• Incorporating Diversity in Active Learning with Support Vector MachinesKlaus Brinker. 59-66
• The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning MethodsGavin Brown, Jeremy L. Wyatt. 67-74
• Tractable Bayesian Learning of Tree Augmented Naive Bayes ModelsJesús Cerquides, Ramon López de Mántaras. 75-82
• AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary OpponentsVincent Conitzer, Tuomas Sandholm. 83-90
• BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum GamesVincent Conitzer, Tuomas Sandholm. 91-98
• Semi-Supervised Learning of Mixture ModelsFabio Gagliardi Cozman, Ira Cohen, Marcelo Cesar Cirelo. 99-106
• On Kernel Methods for Relational LearningChad M. Cumby, Dan Roth. 107-114
• Fast Query-Optimized Kernel Machine Classification Via Incremental Approximate Nearest Support VectorsDennis DeCoste, Dominic Mazzoni. 115-122
• Relational Instance Based Regression for Relational Reinforcement LearningKurt Driessens, Jan Ramon. 123-130
• Design for an Optimal ProbeMichael O. Duff. 131-138
• Diffusion Approximation for Bayesian Markov ChainsMichael O. Duff. 139-146
• Using the Triangle Inequality to Accelerate k-MeansCharles Elkan. 147-153
• Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference LearningYaakov Engel, Shie Mannor, Ron Meir. 154-161
• Action Elimination and Stopping Conditions for Reinforcement LearningEyal Even-Dar, Shie Mannor, Yishay Mansour. 162-169
• Utilizing Domain Knowledge in NeuroevolutionJames Fan, Raymond Lau, Risto Miikkulainen. 170-177
• Boosting Lazy Decision TreesXiaoli Zhang Fern, Carla E. Brodley. 178-185
• Random Projection for High Dimensional Data Clustering: A Cluster Ensemble ApproachXiaoli Zhang Fern, Carla E. Brodley. 186-193
• The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC IsometricsPeter A. Flach. 194-201
• An Analysis of Rule Evaluation MetricsJohannes Fürnkranz, Peter A. Flach. 202-209
• Margin Distribution and LearningAshutosh Garg, Dan Roth. 210-217
• Perceptron Based Learning with Example Dependent and Noisy CostsPeter Geibel, Fritz Wysotzki. 218-225
• Hierarchical Policy Gradient AlgorithmsMohammad Ghavamzadeh, Sridhar Mahadevan. 226-233
• Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential EquationsThore Graepel. 234-241
• Correlated Q-LearningAmy R. Greenwald, Keith Hall. 242-249
• Online Ranking/Collaborative Filtering Using the Perceptron AlgorithmEdward F. Harrington. 250-257
• Goal-directed Learning to FlyAndrew Isaac, Claude Sammut. 258-265
• Probabilistic Classifiers and the Concepts They RecognizeManfred Jaeger. 266-273
• Avoiding Bias when Aggregating Relational Data with Degree DisparityDavid Jensen, Jennifer Neville, Michael Hay. 274-281
• A Faster Iterative Scaling Algorithm for Conditional Exponential ModelRong Jin, Rong Yan, Jian Zhang, Alexander G. Hauptmann. 282-289
• Transductive Learning via Spectral Graph PartitioningThorsten Joachims. 290-297
• Evolving Strategies for Focused Web CrawlingJudy Johnson, Kostas Tsioutsiouliklis, C. Lee Giles. 298-305
• Exploration in Metric State SpacesSham Kakade, Michael J. Kearns, John Langford. 306-312
• Representational Issues in Meta-LearningAlexandros Kalousis, Melanie Hilario. 313-320
• Marginalized Kernels Between Labeled GraphsHisashi Kashima, Koji Tsuda, Akihiro Inokuchi. 321-328
• Informative Discriminant AnalysisSamuel Kaski, Jaakko Peltonen. 329-336
• Characteristics of Long-term Learning in Soar and its Application to the Utility ProblemWilliam G. Kennedy, Kenneth A. De Jong. 337-344
• Unsupervised Learning with Permuted DataSergey Kirshner, Sridevi Parise, Padhraic Smyth. 345-352
• Discriminative Gaussian Mixture Models: A Comparison with Kernel ClassifiersAldebaro Klautau, Nikola Jevtic, Alon Orlitsky. 353-360
• A Kernel Between Sets of VectorsRisi Imre Kondor, Tony Jebara. 361-368
• The Significance of Temporal-Difference Learning in Self-Play Training TD-Rummy versus EVO-rummyClifford Kotnik, Jugal K. Kalita. 369-375
• Visual Learning by Evolutionary Feature SynthesisKrzysztof Krawiec, Bir Bhanu. 376-383
• Classification of Text Documents Based on Minimum System EntropyRaghu Krishnapuram, Krishna Prasad Chitrapura, Sachindra Joshi. 384-391
• Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration QueriesJeremy Kubica, Andrew W. Moore, David Cohn, Jeff G. Schneider. 392-399
• Learning with Idealized KernelsJames T. Kwok, Ivor W. Tsang. 400-407
• The Pre-Image Problem in Kernel MethodsJames T. Kwok, Ivor W. Tsang. 408-415
• Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC CurvesNicolas Lachiche, Peter A. Flach. 416-423
• Reinforcement Learning as Classification: Leveraging Modern ClassifiersMichail G. Lagoudakis, Ronald Parr. 424-431
• Robust Induction of Process Models from Time-Series DataPat Langley, Dileep George, Stephen D. Bay, Kazumi Saito. 432-439
• The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of ShapingAdam Laud, Gerald DeJong. 440-447
• Learning with Positive and Unlabeled Examples Using Weighted Logistic RegressionWee Sun Lee, Bing Liu. 448-455
• Linear Programming Boosting for Uneven DatasetsJure Leskovec, John Shawe-Taylor. 456-463
• Text Classification Using Stochastic Keyword GenerationCong Li, Ji-Rong Wen, Hang Li. 464-471
• A Loss Function Analysis for Classification Methods in Text CategorizationFan Li, Yiming Yang. 472-479
• Decision Tree with Better RankingCharles X. Ling, Robert J. Yan. 480-487
• An Evaluation on Feature Selection for Text ClusteringTao Liu, Shengping Liu, Zheng Chen, Wei-Ying Ma. 488-495
• Link-based ClassificationQing Lu, Lise Getoor. 496-503
• Hierarchical Latent Knowledge Analysis for Co-occurrence DataHiroshi Mamitsuka. 504-511
• The Cross Entropy Method for Fast Policy SearchShie Mannor, Reuven Y. Rubinstein, Yohai Gat. 512-519
• The Set Covering Machine with Data-Dependent Half-SpacesMario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova. 520-527
• Identifying Predictive Structures in Relational Data Using Multiple Instance LearningAmy McGovern, David Jensen. 528-535
• Planning in the Presence of Cost Functions Controlled by an AdversaryH. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum. 536-543
• Using Linear-threshold Algorithms to Combine Multi-class Sub-expertsChris Mesterharm. 544-551
• Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure LearningAndrew W. Moore, Weng-Keen Wong. 552-559
• Error Bounds for Approximate Policy IterationRémi Munos. 560-567
• Machine Learning with HyperkernelsCheng Soon Ong, Alex J. Smola. 568-575
• Mixtures of Conditional Maximum Entropy ModelsDmitry Pavlov, Alexandrin Popescul, David M. Pennock, Lyle H. Ungar. 584-591
• Online Feature Selection using GraftingSimon Perkins, James Theiler. 592-599
• Weighted Order Statistic Classifiers with Large Rank-Order MarginReid B. Porter, Damian Eads, Don R. Hush, James Theiler. 600-607
• Relativized Options: Choosing the Right TransformationBalaraman Ravindran, Andrew G. Barto. 608-615
• Tackling the Poor Assumptions of Naive Bayes Text ClassifiersJason D. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger. 616-623
• Learning with Knowledge from Multiple ExpertsMatthew Richardson, Pedro Domingos. 624-631
• Combining TD-learning with Cascade-correlation NetworksFrançois Rivest, Doina Precup. 632-639
• Kernel PLS-SVC for Linear and Nonlinear ClassificationRoman Rosipal, Leonard J. Trejo, Bryan Matthews. 640-647
• Stochastic Local Search in k-Term DNF LearningUlrich Rückert, Stefan Kramer. 648-655
• Q-Decomposition for Reinforcement Learning AgentsStuart J. Russell, Andrew Zimdars. 656-663
• Adaptive Overrelaxed Bound Optimization MethodsRuslan Salakhutdinov, Sam T. Roweis. 664-671
• Optimization with EM and Expectation-Conjugate-GradientRuslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahramani. 672-679
• TD(0) Converges Provably Faster than the Residual Gradient AlgorithmRalf Schoknecht, Artur Merke. 680-687
• On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy DataMarc Sebban, Jean-Christophe Janodet. 688-695
• Text Bundling: Statistics Based Data-ReductionLawrence Shih, Jason D. Rennie, Yu-Han Chang, David R. Karger. 696-703
• Flexible Mixture Model for Collaborative FilteringLuo Si, Rong Jin. 704-711
• Learning Predictive State RepresentationsSatinder P. Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, Peter Stone. 712-719
• Weighted Low-Rank ApproximationsNathan Srebro, Tommi Jaakkola. 720-727
• Learning To Cooperate in a Social Dilemma: A Satisficing Approach to BargainingJeff L. Stimpson, Michael A. Goodrich. 728-735
• Evolutionary MCMC Sampling and Optimization in Discrete SpacesMalcolm J. A. Strens. 736-743
• Learning on the Test Data: Leveraging Unseen FeaturesBenjamin Taskar, Ming Fai Wong, Daphne Koller. 744-751
• Low Bias Bagged Support Vector MachinesGiorgio Valentini, Thomas G. Dietterich. 752-759
• SimpleSVMS. V. N. Vishwanathan, Alex J. Smola, M. Narasimha Murty. 760-767
• Testing Exchangeability On-LineVladimir Vovk, Ilia Nouretdinov, Alexander Gammerman. 768-775
• Model-based Policy Gradient Reinforcement LearningXin Wang, Thomas G. Dietterich. 776-783
• Learning Mixture Models with the Latent Maximum Entropy PrincipleShaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao. 784-791
• Principled Methods for Advising Reinforcement Learning AgentsEric Wiewiora, Garrison W. Cottrell, Charles Elkan. 792-799
• DISTILL: Learning Domain-Specific Planners by ExampleElly Winner, Manuela M. Veloso. 800-807
• Bayesian Network Anomaly Pattern Detection for Disease OutbreaksWeng-Keen Wong, Andrew W. Moore, Gregory F. Cooper, Michael Wagner. 808-815
• Adaptive Feature-Space Conformal Transformation for Imbalanced-Data LearningGang Wu, Edward Y. Chang. 816-823
• New í-Support Vector Machines and their Sequential Minimal OptimizationXiaoyun Wu, Rohini K. Srihari. 824-831
• Cross-Entropy Directed Embedding of Network DataTakeshi Yamada, Kazumi Saito, Naonori Ueda. 832-839
• Decision-tree Induction from Time-series Data Based on a Standard-example Split TestYuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi. 840-847
• Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney StatisticLian Yan, Robert H. Dodier, Michael Mozer, Richard H. Wolniewicz. 848-855
• Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter SolutionLei Yu, Huan Liu. 856-863
• Isometric Embedding and Continuum ISOMAPHongyuan Zha, Zhenyue Zhang. 864-871
• Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean RepresentationZhihua Zhang. 872-879
• Learning from Attribute Value Taxonomies and Partially Specified InstancesJun Zhang 0002, Vasant Honavar. 880-887
• Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text CategorizationJian Zhang, Rong Jin, Yiming Yang, Alexander G. Hauptmann. 888-895
• Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active LearningYi Zhang, Wei Xu, James P. Callan. 896-903
• On the Convergence of Boosting ProceduresTong Zhang, Bin Yu. 904-911
• Semi-Supervised Learning Using Gaussian Fields and Harmonic FunctionsXiaojin Zhu, Zoubin Ghahramani, John D. Lafferty. 912-919
• Eliminating Class Noise in Large DatasetsXingquan Zhu, Xindong Wu, Qijun Chen. 920-927
• Online Convex Programming and Generalized Infinitesimal Gradient AscentMartin Zinkevich. 928-936