16. ECML 2005: Porto, Portugal

Invited Talks

• Michael R. Berthold:
  Data Analysis in the Life Sciences - Sparking Ideas -. 1
  
• Claire Cardie:
  Machine Learning for Natural Language Processing (and Vice Versa?). 2
  
• Luc De Raedt:
  Statistical Relational Learning: An Inductive Logic Programming Perspective. 3-5
  
• Eamonn J. Keogh:
  Recent Advances in Mining Time Series Data. 6
  
• Ron Kohavi:
  Focus the Mining Beacon: Lessons and Challenges from the World of E-Commerce. 7
  
• Yossi Matias:
  Data Streams and Data Synopses for Massive Data Sets. 8-9
  

Long Papers

• Liviu Badea:
  Clustering and Metaclustering with Nonnegative Matrix Decompositions. 10-22
  
• Christian Bessière, Remi Coletta, Frédéric Koriche, Barry O'Sullivan:
  A SAT-Based Version Space Algorithm for Acquiring Constraint Satisfaction Problems. 23-34
  
• Steffen Bickel, Tobias Scheffer:
  Estimation of Mixture Models Using Co-EM. 35-46
  
• Matthew Brand:
  Nonrigid Embeddings for Dimensionality Reduction. 47-59
  
• Ulf Brefeld, Christoph Büscher, Tobias Scheffer:
  Multi-view Discriminative Sequential Learning. 60-71
  
• Jesús Cerquides, Ramon López de Mántaras:
  Robust Bayesian Linear Classifier Ensembles. 72-83
  
• Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Vítor Santos Costa:
  An Integrated Approach to Learning Bayesian Networks of Rules. 84-95
  
• Isabel Drost, Tobias Scheffer:
  Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam. 96-107
  
• Arkady Epshteyn, Gerald DeJong:
  Rotational Prior Knowledge for SVMs. 108-119
  
• Stefano Ferilli, Teresa Maria Altomare Basile, Nicola Di Mauro, Floriana Esposito:
  On the LearnAbility of Abstraction Theories from Observations for Relational Learning. 120-132
  
• George Forman, Ira Cohen:
  Beware the Null Hypothesis: Critical Value Tables for Evaluating Classifiers. 133-145
  
• Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
  Kernel Basis Pursuit. 146-157
  
• Iris Hendrickx, Antal van den Bosch:
  Hybrid Algorithms with Instance-Based Classification. 158-169
  
• Aloak Kapoor, Russell Greiner:
  Learning and Classifying Under Hard Budgets. 170-181
  
• Wolf Kienzle, Bernhard Schölkopf:
  Training Support Vector Machines with Multiple Equality Constraints. 182-193
  
• Hans van Kuilenburg, Marco Wiering, Marten den Uyl:
  A Model Based Method for Automatic Facial Expression Recognition. 194-205
  
• François Laviolette, Mario Marchand, Mohak Shah:
  Margin-Sparsity Trade-Off for the Set Covering Machine. 206-217
  
• Xiaoli Li, Bing Liu:
  Learning from Positive and Unlabeled Examples with Different Data Distributions. 218-229
  
• Shiau Hong Lim, Gerald DeJong:
  Towards Finite-Sample Convergence of Direct Reinforcement Learning. 230-241
  
• Hsuan-Tien Lin, Ling Li:
  Infinite Ensemble Learning with Support Vector Machines. 242-254
  
• Siwei Lyu:
  A Kernel Between Unordered Sets of Data: The Gaussian Mixture Approach. 255-267
  
• Prem Melville, Stewart M. Yang, Maytal Saar-Tsechansky, Raymond J. Mooney:
  Active Learning for Probability Estimation Using Jensen-Shannon Divergence. 268-279
  
• Jan Peters, Sethu Vijayakumar, Stefan Schaal:
  Natural Actor-Critic. 280-291
  
• Detlef Prescher:
  Inducing Head-Driven PCFGs with Latent Heads: Refining a Tree-Bank Grammar for Parsing. 292-304
  
• Stefan Raeymaekers, Maurice Bruynooghe, Jan Van den Bussche:
  Learning (k, l)-Contextual Tree Languages for Information Extraction. 305-316
  
• Martin Riedmiller:
  Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method. 317-328
  
• Carsten Riggelsen:
  MCMC Learning of Bayesian Network Models by Markov Blanket Decomposition. 329-340
  
• Jarkko Salojärvi, Kai Puolamäki, Samuel Kaski:
  On Discriminative Joint Density Modeling. 341-352
  
• Guy Shani, Ronen I. Brafman, Solomon Eyal Shimony:
  Model-Based Online Learning of POMDPs. 353-364
  
• Shengli Sheng, Charles X. Ling, Qiang Yang:
  Simple Test Strategies for Cost-Sensitive Decision Trees. 365-376
  
• JaeMo Sung, Sung Yang Bang, Seungjin Choi, Zoubin Ghahramani:
  U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models. 377-388
  
• Daniel Szer, François Charpillet:
  An Optimal Best-First Search Algorithm for Solving Infinite Horizon DEC-POMDPs. 389-399
  
• Kari Torkkola, Eugene Tuv:
  Ensemble Learning with Supervised Kernels. 400-411
  
• Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richard Maclin:
  Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another. 412-424
  
• Ronan Trepos, Ansaf Salleb, Marie-Odile Cordier, Véronique Masson, Chantal Gascuel:
  A Distance-Based Approach for Action Recommendation. 425-436
  
• Joannès Vermorel, Mehryar Mohri:
  Multi-armed Bandit Algorithms and Empirical Evaluation. 437-448
  
• Gang Wang, Zhihua Zhang, Frederick H. Lochovsky:
  Annealed Discriminant Analysis. 449-460
  
• Shijun Wang, Changshui Zhang:
  Network Game and Boosting. 461-472
  
• Olcay Taner Yildiz, Ethem Alpaydin:
  Model Selection in Omnivariate Decision Trees. 473-484
  
• Marie desJardins, Priyang Rathod, Lise Getoor:
  Bayesian Network Learning with Abstraction Hierarchies and Context-Specific Independence. 485-496
  

Short Papers

• Steffen Bickel, Peter Haider, Tobias Scheffer:
  Learning to Complete Sentences. 497-504
  
• Antoine Bordes, Léon Bottou:
  The Huller: A Simple and Efficient Online SVM. 505-512
  
• Jérôme Callut, Pierre Dupont:
  Inducing Hidden Markov Models to Model Long-Term Dependencies. 513-521
  
• François Coste, Goulven Kerbellec:
  A Similar Fragments Merging Approach to Learn Automata on Proteins. 522-529
  
• Chris H. Q. Ding, Xiaofeng He, Horst D. Simon:
  Nonnegative Lagrangian Relaxation of K-Means and Spectral Clustering. 530-538
  
• Chris Drummond, Robert C. Holte:
  Severe Class Imbalance: Why Better Algorithms Aren't the Answer. 539-546
  
• Tapio Elomaa, Jussi Kujala, Juho Rousu:
  Approximation Algorithms for Minimizing Empirical Error by Axis-Parallel Hyperplanes. 547-555
  
• Daan Fierens, Jan Ramon, Hendrik Blockeel, Maurice Bruynooghe:
  A Comparison of Approaches for Learning Probability Trees. 556-563
  
• George Forman:
  Counting Positives Accurately Despite Inaccurate Classification. 564-575
  
• Ramunas Girdziusas, Jorma Laaksonen:
  Optimal Stopping and Constraints for Diffusion Models of Signals with Discontinuities. 576-583
  
• Ali Hamzeh, Adel Rahmani:
  An Evolutionary Function Approximation Approach to Compute Prediction in XCSF. 584-592
  
• Masoumeh T. Izadi, Doina Precup:
  Using Rewards for Belief State Updates in Partially Observable Markov Decision Processes. 593-600
  
• Robin Jaulmes, Joelle Pineau, Doina Precup:
  Active Learning in Partially Observable Markov Decision Processes. 601-608
  
• Sergio Jiménez, Fernando Fernández, Daniel Borrajo:
  Machine Learning of Plan Robustness Knowledge About Instances. 609-616
  
• Arnaud Lallouet, Andrei Legtchenko:
  Two Contributions of Constraint Programming to Machine Learning. 617-624
  
• Tao Li, Wei Peng:
  A Clustering Model Based on Matrix Approximation with Applications to Cluster System Log Files. 625-632
  
• Fletcher Lu, J. Efrim Boritz:
  Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford's Law Distributions. 633-640
  
• Ingo Mierswa, Michael Wurst:
  Efficient Case Based Feature Construction. 641-648
  
• Richard Nock, Frank Nielsen:
  Fitting the Smallest Enclosing Bregman Ball. 649-656
  
• Daniel Oblinger, Vittorio Castelli, Tessa A. Lau, Lawrence D. Bergman:
  Similarity-Based Alignment and Generalization. 657-664
  
• Oleg Okun, Helen Priisalu, Alexessander Alves:
  Fast Non-negative Dimensionality Reduction for Protein Fold Recognition. 665-672
  
• Irene M. Ong, Inês de Castro Dutra, David Page, Vítor Santos Costa:
  Mode Directed Path Finding. 673-681
  
• Xuan Hieu Phan, Minh Le Nguyen, Susumu Horiguchi, Tu Bao Ho, Yasushi Inoguchi:
  Classification with Maximum Entropy Modeling of Predictive Association Rules. 682-689
  
• Joaquim F. Pinto da Costa, Jaime S. Cardoso:
  Classification of Ordinal Data Using Neural Networks. 690-697
  
• Barnabás Póczos, Bálint Takács, András Lörincz:
  Independent Subspace Analysis on Innovations. 698-706
  
• Ricardo Rocha, Nuno A. Fonseca, Vítor Santos Costa:
  On Applying Tabling to Inductive Logic Programming. 707-714
  
• *** paper retracted by the authors *** [Learning Models of Relational Stochastic Processes]. 715-723
  
• Surendra K. Singhi, Huan Liu:
  Error-Sensitive Grading for Model Combination. 724-732
  
• Hendrik Skubch, Michael Thielscher:
  Strategy Learning for Reasoning Agents. 733-740
  
• Yuk Lai Suen, Prem Melville, Raymond J. Mooney:
  Combining Bias and Variance Reduction Techniques for Regression Trees. 741-749
  
• Petroula Tsampouka, John Shawe-Taylor:
  Analysis of Generic Perceptron-Like Large Margin Classifiers. 750-758
  
• Fang Wang, Yuhui Qiu:
  Multimodal Function Optimizing by a New Hybrid Nonlinear Simplex Search and Particle Swarm Algorithm. 759-766
  

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