Table of Contents

• Exploration and apprenticeship learning in reinforcement learningPieter Abbeel, Andrew Y. Ng. 1-8 [doi]
• Active learning for Hidden Markov Models: objective functions and algorithmsBrigham Anderson, Andrew Moore. 9-16 [doi]
• Tempering for Bayesian C&RTNicos Angelopoulos, James Cussens. 17-24 [doi]
• Fast condensed nearest neighbor ruleFabrizio Angiulli. 25-32 [doi]
• Predictive low-rank decomposition for kernel methodsFrancis R. Bach, Michael I. Jordan. 33-40 [doi]
• Multi-way distributional clustering via pairwise interactionsRon Bekkerman, Ran El-Yaniv, Andrew McCallum. 41-48 [doi]
• Error limiting reductions between classification tasksAlina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, Bianca Zadrozny. 49-56 [doi]
• Multi-instance tree learningHendrik Blockeel, David Page, Ashwin Srinivasan. 57-64 [doi]
• Action respecting embeddingMichael H. Bowling, Ali Ghodsi, Dana F. Wilkinson. 65-72 [doi]
• Clustering through ranking on manifoldsMarkus Breitenbach, Gregory Z. Grudic. 73-80 [doi]
• Reducing overfitting in process model inductionWill Bridewell, Narges Bani Asadi, Pat Langley, Ljupco Todorovski. 81-88 [doi]
• Learning to rank using gradient descentChristopher J. C. Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, Gregory N. Hullender. 89-96 [doi]
• Learning class-discriminative dynamic Bayesian networksJohn Burge, Terran Lane. 97-104 [doi]
• Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMMSylvain Calinon, Aude Billard. 105-112 [doi]
• Predicting probability distributions for surf height using an ensemble of mixture density networksMichael Carney, Padraig Cunningham, Jim Dowling, Ciaran Lee. 113-120 [doi]
• Hedged learning: regret-minimization with learning expertsYu-Han Chang, Leslie Pack Kaelbling. 121-128 [doi]
• Variational Bayesian image modellingLi Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang. 129-136 [doi]
• Preference learning with Gaussian processesWei Chu, Zoubin Ghahramani. 137-144 [doi]
• New approaches to support vector ordinal regressionWei Chu, S. Sathiya Keerthi. 145-152 [doi]
• A general regression technique for learning transductionsCorinna Cortes, Mehryar Mohri, Jason Weston. 153-160 [doi]
• Learning to compete, compromise, and cooperate in repeated general-sum gamesJacob W. Crandall, Michael A. Goodrich. 161-168 [doi]
• Learning as search optimization: approximate large margin methods for structured predictionHal Daumé III, Daniel Marcu. 169-176 [doi]
• Multimodal oriented discriminant analysisFernando De la Torre, Takeo Kanade. 177-184 [doi]
• A practical generalization of Fourier-based learningAdam Drake, Dan Ventura. 185-192 [doi]
• Combining model-based and instance-based learning for first order regressionKurt Driessens, Saso Dzeroski. 193-200 [doi]
• Reinforcement learning with Gaussian processesYaakov Engel, Shie Mannor, Ron Meir. 201-208 [doi]
• Experimental comparison between bagging and Monte Carlo ensemble classificationRoberto Esposito, Lorenza Saitta. 209-216 [doi]
• Supervised clustering with support vector machinesThomas Finley, Thorsten Joachims. 217-224 [doi]
• Optimal assignment kernels for attributed molecular graphsHolger Fröhlich, Jörg K. Wegner, Florian Sieker, Andreas Zell. 225-232 [doi]
• Closed-form dual perturb and combine for tree-based modelsPierre Geurts, Louis Wehenkel. 233-240 [doi]
• Hierarchic Bayesian models for kernel learningMark Girolami, Simon Rogers. 241-248 [doi]
• Online feature selection for pixel classificationKaren A. Glocer, Damian Eads, James Theiler. 249-256 [doi]
• Learning strategies for story comprehension: a reinforcement learning approachEugene Grois, David C. Wilkins. 257-264 [doi]
• Near-optimal sensor placements in Gaussian processesCarlos Guestrin, Andreas Krause, Ajit Paul Singh. 265-272 [doi]
• Robust one-class clustering using hybrid global and local searchGunjan Gupta, Joydeep Ghosh. 273-280 [doi]
• Statistical and computational analysis of locality preserving projectionXiaofei He, Deng Cai, Wanli Min. 281-288 [doi]
• Intrinsic dimensionality estimation of submanifolds in R:::d:::Matthias Hein, Jean-Yves Audibert. 289-296 [doi]
• Bayesian hierarchical clusteringKatherine A. Heller, Zoubin Ghahramani. 297-304 [doi]
• Online learning over graphsMark Herbster, Massimiliano Pontil, Lisa Wainer. 305-312 [doi]
• Adapting two-class support vector classification methods to many class problemsSimon I. Hill, Arnaud Doucet. 313-320 [doi]
• A martingale framework for concept change detection in time-varying data streamsShen-Shyang Ho. 321-327 [doi]
• Multi-class protein fold recognition using adaptive codesEugene Ie, Jason Weston, William Stafford Noble, Christina S. Leslie. 329-336 [doi]
• Learning approximate preconditions for methods in hierarchical plansOkhtay Ilghami, Héctor Muñoz-Avila, Dana S. Nau, David W. Aha. 337-344 [doi]
• Evaluating machine learning for information extractionNeil Ireson, Fabio Ciravegna, Mary Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli. 345-352 [doi]
• Learn to weight terms in information retrieval using category informationRong Jin, Joyce Y. Chai, Luo Si. 353-360 [doi]
• A smoothed boosting algorithm using probabilistic output codesRong Jin, Jian Zhang. 361-368 [doi]
• Efficient discriminative learning of Bayesian network classifier via boosted augmented naive BayesYushi Jing, Vladimir Pavlovic, James M. Rehg. 369-376 [doi]
• A support vector method for multivariate performance measuresThorsten Joachims. 377-384 [doi]
• Error bounds for correlation clusteringThorsten Joachims, John E. Hopcroft. 385-392 [doi]
• Interactive learning of mappings from visual percepts to actionsSébastien Jodogne, Justus H. Piater. 393-400 [doi]
• A causal approach to hierarchical decomposition of factored MDPsAnders Jonsson, Andrew G. Barto. 401-408 [doi]
• A comparison of tight generalization error boundsMatti Kääriäinen, John Langford. 409-416 [doi]
• Generalized LARS as an effective feature selection tool for text classification with SVMsS. Sathiya Keerthi. 417-424 [doi]
• Ensembles of biased classifiersRinat Khoussainov, Andreas Heß, Nicholas Kushmerick. 425-432 [doi]
• Computational aspects of Bayesian partition modelsMikko Koivisto, Kismat Sood. 433-440 [doi]
• Learning the structure of Markov logic networksStanley Kok, Pedro Domingos. 441-448 [doi]
• Using additive expert ensembles to cope with concept driftJeremy Z. Kolter, Marcus A. Maloof. 449-456 [doi]
• Semi-supervised graph clustering: a kernel approachBrian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney. 457-464 [doi]
• A brain computer interface with online feedback based on magnetoencephalographyThomas Navin Lal, Michael Schröder 0002, N. Jeremy Hill, Hubert Preißl, Thilo Hinterberger, Jürgen Mellinger, Martin Bogdan, Wolfgang Rosenstiel, Thomas Hofmann, Niels Birbaumer, Bernhard Schölkopf. 465-472 [doi]
• Relating reinforcement learning performance to classification performanceJohn Langford, Bianca Zadrozny. 473-480 [doi]
• PAC-Bayes risk bounds for sample-compressed Gibbs classifiersFrançois Laviolette, Mario Marchand. 481-488 [doi]
• Heteroscedastic Gaussian process regressionQuoc V. Le, Alexander J. Smola, Stéphane Canu. 489-496 [doi]
• Predicting relative performance of classifiers from samplesRui Leite, Pavel Brazdil. 497-503 [doi]
• Logistic regression with an auxiliary data sourceXuejun Liao, Ya Xue, Lawrence Carin. 505-512 [doi]
• Predicting protein folds with structural repeats using a chain graph modelYan Liu, Eric P. Xing, Jaime G. Carbonell. 513-520 [doi]
• Unsupervised evidence integrationPhilip M. Long, Vinay Varadan, Sarah Gilman, Mark Treshock, Rocco A. Servedio. 521-528 [doi]
• Naive Bayes models for probability estimationDaniel Lowd, Pedro Domingos. 529-536 [doi]
• ROC confidence bands: an empirical evaluationSofus A. Macskassy, Foster J. Provost, Saharon Rosset. 537-544 [doi]
• Modeling word burstiness using the Dirichlet distributionRasmus Elsborg Madsen, David Kauchak, Charles Elkan. 545-552 [doi]
• Proto-value functions: developmental reinforcement learningSridhar Mahadevan. 553-560 [doi]
• The cross entropy method for classificationShie Mannor, Dori Peleg, Reuven Y. Rubinstein. 561-568 [doi]
• Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guaranteesH. Brendan McMahan, Maxim Likhachev, Geoffrey J. Gordon. 569-576 [doi]
• Comparing clusterings: an axiomatic viewMarina Meila. 577-584 [doi]
• Weighted decomposition kernelsSauro Menchetti, Fabrizio Costa, Paolo Frasconi. 585-592 [doi]
• High speed obstacle avoidance using monocular vision and reinforcement learningJeff Michels, Ashutosh Saxena, Andrew Y. Ng. 593-600 [doi]
• Dynamic preferences in multi-criteria reinforcement learningSriraam Natarajan, Prasad Tadepalli. 601-608 [doi]
• Learning first-order probabilistic models with combining rulesSriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar. 609-616 [doi]
• An efficient method for simplifying support vector machinesDucDung Nguyen, Tu Bao Ho. 617-624 [doi]
• Predicting good probabilities with supervised learningAlexandru Niculescu-Mizil, Rich Caruana. 625-632 [doi]
• Recycling data for multi-agent learningSantiago Ontañón, Enric Plaza. 633-640 [doi]
• A graphical model for chord progressions embedded in a psychoacoustic spaceJean-François Paiement, Douglas Eck, Samy Bengio, David Barber. 641-648 [doi]
• Q-learning of sequential attention for visual object recognition from informative local descriptorsLucas Paletta, Gerald Fritz, Christin Seifert. 649-656 [doi]
• Discriminative versus generative parameter and structure learning of Bayesian network classifiersFranz Pernkopf, Jeff A. Bilmes. 657-664 [doi]
• Optimizing abstaining classifiers using ROC analysisTadeusz Pietraszek. 665-672 [doi]
• Independent subspace analysis using geodesic spanning treesBarnabás Póczos, András Lörincz. 673-680 [doi]
• A model for handling approximate, noisy or incomplete labeling in text classificationGanesh Ramakrishnan, Krishna Prasad Chitrapura, Raghu Krishnapuram, Pushpak Bhattacharyya. 681-688 [doi]
• Healing the relevance vector machine through augmentationCarl Edward Rasmussen, Joaquin Quiñonero Candela. 689-696 [doi]
• Supervised versus multiple instance learning: an empirical comparisonSoumya Ray, Mark Craven. 697-704 [doi]
• Generalized skewing for functions with continuous and nominal attributesSoumya Ray, David Page. 705-712 [doi]
• Fast maximum margin matrix factorization for collaborative predictionJason D. M. Rennie, Nathan Srebro. 713-719 [doi]
• Coarticulation: an approach for generating concurrent plans in Markov decision processesKhashayar Rohanimanesh, Sridhar Mahadevan. 720-727 [doi]
• Why skewing works: learning difficult Boolean functions with greedy tree learnersBernard Rosell, Lisa Hellerstein, Soumya Ray, David Page. 728-735 [doi]
• Integer linear programming inference for conditional random fieldsDan Roth, Wen-tau Yih. 736-743 [doi]
• Learning hierarchical multi-category text classification modelsJuho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor. 744-751 [doi]
• Expectation maximization algorithms for conditional likelihoodsJarkko Salojärvi, Kai Puolamäki, Samuel Kaski. 752-759 [doi]
• Estimating and computing density based distance metricsSajama, Alon Orlitsky. 760-767 [doi]
• Supervised dimensionality reduction using mixture modelsSajama, Alon Orlitsky. 768-775 [doi]
• Object correspondence as a machine learning problemBernhard Schölkopf, Florian Steinke, Volker Blanz. 776-783 [doi]
• Analysis and extension of spectral methods for nonlinear dimensionality reductionFei Sha, Lawrence K. Saul. 784-791 [doi]
• Non-negative tensor factorization with applications to statistics and computer visionAmnon Shashua, Tamir Hazan. 792-799 [doi]
• Fast inference and learning in large-state-space HMMsSajid M. Siddiqi, Andrew W. Moore. 800-807 [doi]
• New d-separation identification results for learning continuous latent variable modelsRicardo Silva, Richard Scheines. 808-815 [doi]
• Identifying useful subgoals in reinforcement learning by local graph partitioningÖzgür Simsek, Alicia P. Wolfe, Andrew G. Barto. 816-823 [doi]
• Beyond the point cloud: from transductive to semi-supervised learningVikas Sindhwani, Partha Niyogi, Mikhail Belkin. 824-831 [doi]
• Active learning for sampling in time-series experiments with application to gene expression analysisRohit Singh, Nathan Palmer, David K. Gifford, Bonnie Berger, Ziv Bar-Joseph. 832-839 [doi]
• Compact approximations to Bayesian predictive distributionsEdward Snelson, Zoubin Ghahramani. 840-847 [doi]
• Large scale genomic sequence SVM classifiersSören Sonnenburg, Gunnar Rätsch, Bernhard Schölkopf. 848-855 [doi]
• A theoretical analysis of Model-Based Interval EstimationAlexander L. Strehl, Michael L. Littman. 856-863 [doi]
• Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learningQiang Sun, Gerald DeJong. 864-871 [doi]
• Unifying the error-correcting and output-code AdaBoost within the margin frameworkYijun Sun, Sinisa Todorovic, Jian Li, Dapeng Wu. 872-879 [doi]
• Finite time bounds for sampling based fitted value iterationCsaba Szepesvári, Rémi Munos. 880-887 [doi]
• TD(lambda) networks: temporal-difference networks with eligibility tracesBrian Tanner, Richard S. Sutton. 888-895 [doi]
• Learning structured prediction models: a large margin approachBenjamin Taskar, Vassil Chatalbashev, Daphne Koller, Carlos Guestrin. 896-903 [doi]
• Learning discontinuities with products-of-sigmoids for switching between local modelsMarc Toussaint, Sethu Vijayakumar. 904-911 [doi]
• Core Vector Regression for very large regression problemsIvor W. Tsang, James T. Kwok, Kimo T. Lai. 912-919 [doi]
• Propagating distributions on a hypergraph by dual information regularizationKoji Tsuda. 920-927 [doi]
• Hierarchical Dirichlet model for document classificationSriharsha Veeramachaneni, Diego Sona, Paolo Avesani. 928-935 [doi]
• Implicit surface modelling as an eigenvalue problemChristian Walder, Olivier Chapelle, Bernhard Schölkopf. 936-939 [doi]
• New kernels for protein structural motif discovery and function classificationChang Wang, Stephen D. Scott. 940-947 [doi]
• Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fieldsShaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng. 948-955 [doi]
• Bayesian sparse sampling for on-line reward optimizationTao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans. 956-963 [doi]
• Learning predictive representations from a historyEric Wiewiora. 964-971 [doi]
• Incomplete-data classification using logistic regressionDavid Williams, Xuejun Liao, Ya Xue, Lawrence Carin. 972-979 [doi]
• Learning predictive state representations in dynamical systems without resetBritton Wolfe, Michael R. James, Satinder P. Singh. 980-987 [doi]
• Linear Asymmetric Classifier for cascade detectorsJianxin Wu, Matthew D. Mullin, James M. Rehg. 988-995 [doi]
• Building Sparse Large Margin ClassifiersMingrui Wu, Bernhard Schölkopf, Gökhan H. Bakir. 996-1003 [doi]
• Dirichlet enhanced relational learningZhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Peter Kriegel. 1004-1011 [doi]
• Learning Gaussian processes from multiple tasksKai Yu, Volker Tresp, Anton Schwaighofer. 1012-1019 [doi]
• Augmenting naive Bayes for rankingHarry Zhang, Liangxiao Jiang, Jiang Su. 1020-1027 [doi]
• A new Mallows distance based metric for comparing clusteringsDing Zhou, Jia Li, Hongyuan Zha. 1028-1035 [doi]
• Learning from labeled and unlabeled data on a directed graphDengyong Zhou, Jiayuan Huang, Bernhard Schölkopf. 1036-1043 [doi]
• 2D Conditional Random Fields for Web information extractionJun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, Wei-Ying Ma. 1044-1051 [doi]
• Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learningXiaojin Zhu, John D. Lafferty. 1052-1059 [doi]
• Large margin non-linear embeddingAlexander Zien, Joaquin Quiñonero Candela. 1060-1067 [doi]