18. ECML 2007: Warsaw, Poland

Invited Talks (shared with PKDD 2007

• Tom M. Mitchell:
  Learning, Information Extraction and the Web. 1
  
• Peter A. Flach:
  Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation. 2-3
  
• Ricardo A. Baeza-Yates:
  Mining Queries. 4
  
• Barry Smyth:
  Adventures in Personalized Information Access. 5
  

Long Papers

• David Andrzejewski, Anne Mulhern, Ben Liblit, Xiaojin Zhu:
  Statistical Debugging Using Latent Topic Models. 6-17
  
• Leonor Becerra-Bonache, Colin de la Higuera, Jean-Christophe Janodet, Frédéric Tantini:
  Learning Balls of Strings with Correction Queries. 18-29
  
• Paul N. Bennett:
  Neighborhood-Based Local Sensitivity. 30-41
  
• Steffen Börm, Jochen Garcke:
  Approximating Gaussian Processes with H²-Matrices. 42-53
  
• Laurent Boyer, Amaury Habrard, Marc Sebban:
  Learning Metrics Between Tree Structured Data: Application to Image Recognition. 54-66
  
• John Burge, Terran Lane:
  Shrinkage Estimator for Bayesian Network Parameters. 67-78
  
• Xiongcai Cai, Arcot Sowmya:
  Level Learning Set: A Novel Classifier Based on Active Contour Models. 79-90
  
• Jérôme Callut, Pierre Dupont:
  Learning Partially Observable Markov Models from First Passage Times. 91-103
  
• Michael Connor, Dan Roth:
  Context Sensitive Paraphrasing with a Global Unsupervised Classifier. 104-115
  
• Pinar Donmez, Jaime G. Carbonell, Paul N. Bennett:
  Dual Strategy Active Learning. 116-127
  
• Kenneth Dwyer, Robert Holte:
  Decision Tree Instability and Active Learning. 128-139
  
• Derek Greene, Padraig Cunningham:
  Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised Clustering. 140-151
  
• Thomas Gärtner, Gemma C. Garriga:
  The Cost of Learning Directed Cuts. 152-163
  
• Tony Jebara, Yingbo Song, Kapil Thadani:
  Spectral Clustering and Embedding with Hidden Markov Models. 164-175
  
• Angelika Kimmig, Luc De Raedt, Hannu Toivonen:
  Probabilistic Explanation Based Learning. 176-187
  
• Gregory Kuhlmann, Peter Stone:
  Graph-Based Domain Mapping for Transfer Learning in General Games. 188-200
  
• Xiaoli Li, Bing Liu, See-Kiong Ng:
  Learning to Classify Documents with Only a Small Positive Training Set. 201-213
  
• Xiao-Lin Li, Zhi-Hua Zhou:
  Structure Learning of Probabilistic Relational Models from Incomplete Relational Data. 214-225
  
• Dimitrios Mavroeidis, Michalis Vazirgiannis:
  Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA. 226-237
  
• Andreas Nägele, Mathäus Dejori, Martin Stetter:
  Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures. 238-249
  
• Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass:
  Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs. 250-261
  
• Sunho Park, Seungjin Choi:
  Source Separation with Gaussian Process Models. 262-273
  
• Elisa Ricci, Tijl De Bie, Nello Cristianini:
  Discriminative Sequence Labeling by Z-Score Optimization. 274-285
  
• Mark W. Schmidt, Glenn Fung, Rómer Rosales:
  Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches. 286-297
  
• Matthias Seeger, Sebastian Gerwinn, Matthias Bethge:
  Bayesian Inference for Sparse Generalized Linear Models. 298-309
  
• David B. Skalak, Alexandru Niculescu-Mizil, Rich Caruana:
  Classifier Loss Under Metric Uncertainty. 310-322
  
• Daria Sorokina, Rich Caruana, Mirek Riedewald:
  Additive Groves of Regression Trees. 323-334
  
• Alessandro Sperduti:
  Efficient Computation of Recursive Principal Component Analysis for Structured Input. 335-346
  
• Harald Steck:
  Hinge Rank Loss and the Area Under the ROC Curve. 347-358
  
• Jan Struyf, Saso Dzeroski:
  Clustering Trees with Instance Level Constraints. 359-370
  
• Jan-Nikolas Sulzmann, Johannes Fürnkranz, Eyke Hüllermeier:
  On Pairwise Naive Bayes Classifiers. 371-381
  
• Rikiya Takahashi:
  Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models. 382-393
  
• Stephan Timmer, Martin Riedmiller:
  Safe Q-Learning on Complete History Spaces. 394-405
  
• Grigorios Tsoumakas, Ioannis P. Vlahavas:
  Random k -Labelsets: An Ensemble Method for Multilabel Classification. 406-417
  
• Anneleen Van Assche, Hendrik Blockeel:
  Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble. 418-429
  
• Alexander Vezhnevets, Olga Barinova:
  Avoiding Boosting Overfitting by Removing Confusing Samples. 430-441
  
• Thomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman:
  Planning and Learning in Environments with Delayed Feedback. 442-453
  
• Wei Wang, Zhi-Hua Zhou:
  Analyzing Co-training Style Algorithms. 454-465
  
• Daan Wierstra, Jürgen Schmidhuber:
  Policy Gradient Critics. 466-477
  
• Shaomin Wu, Peter A. Flach, Cèsar Ferri Ramirez:
  An Improved Model Selection Heuristic for AUC. 478-489
  
• Fei Zheng, Geoffrey I. Webb:
  Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators. 490-501
  

Short Papers

• Annalisa Appice, Saso Dzeroski:
  Stepwise Induction of Multi-target Model Trees. 502-509
  
• Paulo J. Azevedo, Alípio Mário Jorge:
  Comparing Rule Measures for Predictive Association Rules. 510-517
  
• Korinna Bade, Marcel Hermkes, Andreas Nürnberger:
  User Oriented Hierarchical Information Organization and Retrieval. 518-526
  
• Sabri Bayoudh, Harold Mouchère, Laurent Miclet, Éric Anquetil:
  Learning a Classifier with Very Few Examples: Analogy Based and Knowledge Based Generation of New Examples for Character Recognition. 527-534
  
• Steven Busuttil, Yuri Kalnishkan:
  Weighted Kernel Regression for Predicting Changing Dependencies. 535-542
  
• András Bánhalmi, András Kocsor, Róbert Busa-Fekete:
  Counter-Example Generation-Based One-Class Classification. 543-550
  
• Mumin Cebe, Cigdem Gunduz Demir:
  Test-Cost Sensitive Classification Based on Conditioned Loss Functions. 551-558
  
• Xiangyu Duan, Jun Zhao, Bo Xu:
  Probabilistic Models for Action-Based Chinese Dependency Parsing. 559-566
  
• Daan Fierens, Jan Ramon, Maurice Bruynooghe, Hendrik Blockeel:
  Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search. 567-574
  
• Peter A. Flach, Edson Takashi Matsubara:
  A Simple Lexicographic Ranker and Probability Estimator. 575-582
  
• Eyke Hüllermeier, Johannes Fürnkranz:
  On Minimizing the Position Error in Label Ranking. 583-590
  
• Goele Hollanders, Geert Jan Bex, Marc Gyssens, Ronald L. Westra, Karl Tuyls:
  On Phase Transitions in Learning Sparse Networks. 591-599
  
• Rong Jin, Ming Wu, Rahul Sukthankar:
  Semi-supervised Collaborative Text Classification. 600-607
  
• Samuel Kaski, Jaakko Peltonen:
  Learning from Relevant Tasks Only. 608-615
  
• Alexandre Klementiev, Dan Roth, Kevin Small:
  An Unsupervised Learning Algorithm for Rank Aggregation. 616-623
  
• Dragi Kocev, Celine Vens, Jan Struyf, Saso Dzeroski:
  Ensembles of Multi-Objective Decision Trees. 624-631
  
• Tilman Lange, Joachim M. Buhmann:
  Kernel-Based Grouping of Histogram Data. 632-639
  
• Rachel Lomasky, Carla E. Brodley, M. Aernecke, D. Walt, Mark A. Friedl:
  Active Class Selection. 640-647
  
• Francis Maes, Ludovic Denoyer, Patrick Gallinari:
  Sequence Labeling with Reinforcement Learning and Ranking Algorithms. 648-657
  
• Sang-Hyeun Park, Johannes Fürnkranz:
  Efficient Pairwise Classification. 658-665
  
• Jin Hyeong Park, Chandan K. Reddy:
  Scale-Space Based Weak Regressors for Boosting. 666-673
  
• Dan Pelleg, Dorit Baras:
  K -Means with Large and Noisy Constraint Sets. 674-682
  
• Katharina Probst, Rayid Ghani:
  Towards 'Interactive' Active Learning in Multi-view Feature Sets for Information Extraction. 683-690
  
• Tapani Raiko, Alexander Ilin, Juha Karhunen:
  Principal Component Analysis for Large Scale Problems with Lots of Missing Values. 691-698
  
• Jan Ramon, Kurt Driessens, Tom Croonenborghs:
  Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling. 699-707
  
• Umaa Rebbapragada, Carla E. Brodley:
  Class Noise Mitigation Through Instance Weighting. 708-715
  
• Ulrich Rückert, Stefan Kramer:
  Optimizing Feature Sets for Structured Data. 716-723
  
• Victor S. Sheng, Charles X. Ling:
  Roulette Sampling for Cost-Sensitive Learning. 724-731
  
• Tomás Singliar, Milos Hauskrecht:
  Modeling Highway Traffic Volumes. 732-739
  
• Zoltán Szabó, Barnabás Póczos, András Lörincz:
  Undercomplete Blind Subspace Deconvolution Via Linear Prediction. 740-747
  
• Jo-Anne Ting, Evangelos Theodorou, Stefan Schaal:
  Learning an Outlier-Robust Kalman Filter. 748-756
  
• Deepak Verma, Rajesh P. N. Rao:
  Imitation Learning Using Graphical Models. 757-764
  
• Marcin Wojnarski:
  Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks. 765-772
  
• Liang Xiong, Fei Wang, Changshui Zhang:
  Semi-definite Manifold Alignment. 773-781
  
• Qubo You, Nanning Zheng, Shaoyi Du, Yang Wu:
  General Solution for Supervised Graph Embedding. 782-789
  
• Amelia Zafra, Sebastián Ventura:
  Multi-objective Genetic Programming for Multiple Instance Learning. 790-797
  
• Monika Záková, Filip Zelezný:
  Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning. 798-805
  

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