Invited Talks

• 
  Charu C. Aggarwal: On Temporal Evolution in Data Streams. 1
• 
  C. Lee Giles: The Future of CiteSeer: CiteSeer^x. 2
• 
  Jonathan Schaeffer: Learning to Have Fun. 3
• 
  Sebastian Thrun: Winning the DARPA Grand Challenge. 4
• 
  Henry Tirri: Challenges of Urban Sensing. 5

Long Papers

• 
  Alon Altman, Avivit Bercovici-Boden, Moshe Tennenholtz: Learning in One-Shot Strategic Form Games. 6-17
• 
  Massih-Reza Amini, Nicolas Usunier, François Laviolette, Alexandre Lacasse, Patrick Gallinari: A Selective Sampling Strategy for Label Ranking. 18-29
• 
  Ron Bekkerman, Mehran Sahami, Erik G. Learned-Miller: Combinatorial Markov Random Fields. 30-41
• 
  Marc Bernard, Amaury Habrard, Marc Sebban: Learning Stochastic Tree Edit Distance. 42-53
• 
  Christopher H. Bryant, Daniel Fredouille, Alex Wilson, Channa K. Jayawickreme, Steven Jupe, Simon Topp: Pertinent Background Knowledge for Learning Protein Grammars. 54-65
• 
  John Burge, Terran Lane: Improving Bayesian Network Structure Search with Random Variable Aggregation Hierarchies. 66-77
• 
  Jérôme Callut, Pierre Dupont: Sequence Discrimination Using Phase-Type Distributions. 78-89
• 
  Alexander Clark, Christophe Costa Florêncio, Chris Watkins: Languages as Hyperplanes: Grammatical Inference with String Kernels. 90-101
• 
  Gerald DeJong: Toward Robust Real-World Inference: A New Perspective on Explanation-Based Learning. 102-113
• 
  Uwe Dick, Kristian Kersting: Fisher Kernels for Relational Data. 114-125
• 
  William Elazmeh, Nathalie Japkowicz, Stan Matwin: Evaluating Misclassifications in Imbalanced Data. 126-137
• 
  Raquel Fuentetaja, Daniel Borrajo: Improving Control-Knowledge Acquisition for Planning by Active Learning. 138-149
• 
  Ricard Gavaldà, Philipp W. Keller, Joelle Pineau, Doina Precup: PAC-Learning of Markov Models with Hidden State. 150-161
• 
  David Grangier, Florent Monay, Samy Bengio: A Discriminative Approach for the Retrieval of Images from Text Queries. 162-173
• 
  Bernd Gutmann, Kristian Kersting: TildeCRF: Conditional Random Fields for Logical Sequences. 174-185
• 
  Corneliu Henegar, Karine Clément, Jean-Daniel Zucker: Unsupervised Multiple-Instance Learning for Functional Profiling of Genomic Data. 186-197
• 
  Aleks Jakulin, Irina Rish: Bayesian Learning of Markov Network Structure. 198-209
• 
  Sébastien Jodogne, Cyril Briquet, Justus H. Piater: Approximate Policy Iteration for Closed-Loop Learning of Visual Tasks. 210-221
• 
  Sébastien Jodogne, Justus H. Piater: Task-Driven Discretization of the Joint Space of Visual Percepts and Continuous Actions. 222-233
• 
  Rasa Jurgelenaite, Tom Heskes: EM Algorithm for Symmetric Causal Independence Models. 234-245
• 
  Ata Kabán, Xin Wang: Deconvolutive Clustering of Markov States. 246-257
• 
  Min Sub Kim, William T. B. Uther: Patching Approximate Solutions in Reinforcement Learning. 258-269
• 
  Nathaniel J. King, Neil D. Lawrence: Fast Variational Inference for Gaussian Process Models Through KL-Correction. 270-281
• 
  Levente Kocsis, Csaba Szepesvári: Bandit Based Monte-Carlo Planning. 282-293
• 
  Jussi Kollin, Mikko Koivisto: Bayesian Learning with Mixtures of Trees. 294-305
• 
  Quoc V. Le, Alexander J. Smola, Thomas Gärtner, Yasemin Altun: Transductive Gaussian Process Regression with Automatic Model Selection. 306-317
• 
  Alessandro Moschitti: Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees. 318-329
• 
  Martin Mozina, Janez Demsar, Jure Zabkar, Ivan Bratko: Why Is Rule Learning Optimistic and How to Correct It. 330-340
• 
  Gisele L. Pappa, Alex Alves Freitas: Automatically Evolving Rule Induction Algorithms. 341-352
• 
  Tobias Pfingsten: Bayesian Active Learning for Sensitivity Analysis. 353-364
• 
  Roberto Santana, Pedro Larrañaga, José Antonio Lozano: Mixtures of Kikuchi Approximations. 365-376
• 
  Martin Scholz: Boosting in PN Spaces. 377-388
• 
  Guy Shani, Ronen I. Brafman, Solomon Eyal Shimony: Prioritizing Point-Based POMDP Solvers. 389-400
• 
  Hyunjung Shin, N. Jeremy Hill, Gunnar Rätsch: Graph Based Semi-supervised Learning with Sharper Edges. 401-412
• 
  Dan Roth, Kevin Small: Margin-Based Active Learning for Structured Output Spaces. 413-424
• 
  Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin: Skill Acquisition Via Transfer Learning and Advice Taking. 425-436
• 
  Petroula Tsampouka, John Shawe-Taylor: Constant Rate Approximate Maximum Margin Algorithms. 437-448
• 
  Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer G. Dy, R. Bharat Rao: Batch Classification with Applications in Computer Aided Diagnosis. 449-460
• 
  Bin Wang, Harry Zhang: Improving the Ranking Performance of Decision Trees. 461-472
• 
  Dong Wang, Jianmin Li, Bo Zhang: Multiple-Instance Learning Via Random Walk. 473-484
• 
  Michael Wurst, Katharina Morik, Ingo Mierswa: Localized Alternative Cluster Ensembles for Collaborative Structuring. 485-496
• 
  Xiao-Bing Xue, Zhi-Hua Zhou: Distributional Features for Text Categorization. 497-508
• 
  Bojun Yan, Carlotta Domeniconi: Subspace Metric Ensembles for Semi-supervised Clustering of High Dimensional Data. 509-520
• 
  Bojun Yan, Carlotta Domeniconi: An Adaptive Kernel Method for Semi-supervised Clustering. 521-532
• 
  Ying Yang, Geoffrey I. Webb, Jesús Cerquides, Kevin B. Korb, Janice R. Boughton, Kai Ming Ting: To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles. 533-544
• 
  Dragomir Yankov, Dennis DeCoste, Eamonn J. Keogh: Ensembles of Nearest Neighbor Forecasts. 545-556

Short Papers

• 
  Will Bridewell, Pat Langley, Steve Racunas, Stuart R. Borrett: Learning Process Models with Missing Data. 557-565
• 
  Klaus Brinker, Eyke Hüllermeier: Case-Based Label Ranking. 566-573
• 
  Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet: Cascade Evaluation of Clustering Algorithms. 574-581
• 
  Michael Carney, Padraig Cunningham: Making Good Probability Estimates for Regression. 582-589
• 
  Bo Chen, Bin Gao, Tie-Yan Liu, Yu-Fu Chen, Wei-Ying Ma: Fast Spectral Clustering of Data Using Sequential Matrix Compression. 590-597
• 
  Antonio D. Chiaravalloti, Gianluigi Greco, Antonella Guzzo, Luigi Pontieri: An Information-Theoretic Framework for High-Order Co-clustering of Heterogeneous Objects. 598-605
• 
  Trevor Cohn: Efficient Inference in Large Conditional Random Fields. 606-613
• 
  Juan C. Cuevas-Tello, Peter Tiño, Somak Raychaudhury: A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses. 614-621
• 
  Jason V. Davis, Jungwoo Ha, Christopher J. Rossbach, Hany E. Ramadan, Emmett Witchel: Cost-Sensitive Decision Tree Learning for Forensic Classification. 622-629
• 
  Alexander N. Dolia, Tijl De Bie, Christopher J. Harris, John Shawe-Taylor, D. M. Titterington: The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces. 630-637
• 
  Byron J. Gao, Martin Ester: Right of Inference: Nearest Rectangle Learning Revisited. 638-645
• 
  Peter Geibel: Reinforcement Learning for MDPs with Constraints. 646-653
• 
  Faustino J. Gomez, Jürgen Schmidhuber, Risto Miikkulainen: Efficient Non-linear Control Through Neuroevolution. 654-662
• 
  Derek Greene, Padraig Cunningham: Efficient Prediction-Based Validation for Document Clustering. 663-670
• 
  Manfred Jaeger: On Testing the Missing at Random Assumption. 671-678
• 
  Tony Jebara, Vlad Shchogolev: B-Matching for Spectral Clustering. 679-686
• 
  Christine Körner, Stefan Wrobel: Multi-class Ensemble-Based Active Learning. 687-694
• 
  Dominic Mazzoni, Kiri Wagstaff, Michael C. Burl: Active Learning with Irrelevant Examples. 695-702
• 
  Georgi I. Nalbantov, Jan C. Bioch, Patrick J. F. Groenen: Classification with Support Hyperplanes. 703-710
• 
  Kee Siong Ng: (Agnostic) PAC Learning Concepts in Higher-Order Logic. 711-718
• 
  Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér: Evaluating Feature Selection for SVMs in High Dimensions. 719-726
• 
  Martin Nyffenegger, Jean-Cédric Chappelier, Éric Gaussier: Revisiting Fisher Kernels for Document Similarities. 727-734
• 
  Scott Proper, Prasad Tadepalli: Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery. 735-742
• 
  Stefan Rüping: Robust Probabilistic Calibration. 743-750
• 
  Guido Sanguinetti, Neil D. Lawrence: Missing Data in Kernel PCA. 751-758
• 
  Péter Schönhofen, András A. Benczúr: Exploiting Extremely Rare Features in Text Categorization. 759-766
• 
  Suvrit Sra: Efficient Large Scale Linear Programming Support Vector Machines. 767-774
• 
  Jan Struyf, Jesse Davis, C. David Page Jr.: An Efficient Approximation to Lookahead in Relational Learners. 775-782
• 
  Gerald Tesauro, Nicholas K. Jong, Rajarshi Das, Mohamed N. Bennani: Improvement of Systems Management Policies Using Hybrid Reinforcement Learning. 783-791
• 
  Ivor W. Tsang, András Kocsor, James T. Kwok: Diversified SVM Ensembles for Large Data Sets. 792-800
• 
  Alexey Tsymbal, Mykola Pechenizkiy, Padraig Cunningham: Dynamic Integration with Random Forests. 801-808
• 
  Anneleen Van Assche, Hendrik Blockeel: Bagging Using Statistical Queries. 809-816
• 
  Samuel Wieczorek, Gilles Bisson, Mirta B. Gordon: Guiding the Search in the NO Region of the Phase Transition Problem with a Partial Subsumption Test. 817-824
• 
  Shiming Xiang, Feiping Nie, Changshui Zhang, Chunxia Zhang: Spline Embedding for Nonlinear Dimensionality Reduction. 825-832
• 
  Jun Xu, Yunbo Cao, Hang Li, Yalou Huang: Cost-Sensitive Learning of SVM for Ranking. 833-840
• 
  Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Kriegel: Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures. 841-848

last updated on Mon May 20 01:55:56 2013 by the dblp team
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