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ORCID17th ECML 2006: Berlin, Germany
- Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou:
Machine Learning: ECML 2006, 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006, Proceedings. Lecture Notes in Computer Science 4212, Springer 2006, ISBN 3-540-45375-X
Invited Talks
- Charu C. Aggarwal:
On Temporal Evolution in Data Streams. 1 - C. Lee Giles:
The Future of CiteSeer: CiteSeerx. 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 John 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 Carlos 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
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