14. ECML 2003: Cavtat-Dubrovnik, Croatia
Nada Lavrac, Dragan Gamberger, Ljupco Todorovski, Hendrik Blockeel (Eds.): Machine Learning: ECML 2003, 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 22-26, 2003, Proceedings. Springer 2003 Lecture Notes in Computer Science ISBN 3-540-20121-1
Invited Papers
- Pieter W. Adriaans: From Knowledge-Based to Skill-Based Systems: Sailing as a Machine Learning Challenge. 1-8
- Leo Breiman: Two-Eyed Algorithms and Problems. 9
- Christos Faloutsos: Next Generation Data Mining Tools: Power Laws and Self-similarity for Graphs, Streams and Traditional Data. 10-15
- Donald B. Rubin: Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketing Interventions. 16-22
Contributed Papers
- Ulf Brefeld, Peter Geibel, Fritz Wysotzki: Support Vector Machines with Example Dependent Costs. 23-34
- Pedro Campos, Thibault Langlois: Abalearn: A Risk-Sensitive Approach to Self-play Learning in Abalone. 35-46
- Chien Chin Chen, Yao-Tsung Chen, Yeali S. Sun, Meng Chang Chen: Life Cycle Modeling of News Events Using Aging Theory. 47-59
- François Coste, Daniel Fredouille: Unambiguous Automata Inference by Means of State-Merging Methods. 60-71
- Paul A. Crook, Gillian Hayes: Could Active Perception Aid Navigation of Partially Observable Grid Worlds? 72-83
- Walter Daelemans, Véronique Hoste, Fien De Meulder, Bart Naudts: Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language. 84-95
- Damien Ernst, Pierre Geurts, Louis Wehenkel: Iteratively Extending Time Horizon Reinforcement Learning. 96-107
- César Ferri, José Hernández-Orallo, Miguel A. Salido: Volume under the ROC Surface for Multi-class Problems. 108-120
- César Ferri, Peter A. Flach, José Hernández-Orallo: Improving the AUC of Probabilistic Estimation Trees. 121-132
- Pascal Garcia: A New Way to Introduce Knowledge into Reinforcement Learning. 157-168
- Amaury Habrard, Marc Bernard, Marc Sebban: Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference. 169-180
- Pieter Jan't Hoen, Sander M. Bohte: COllective INtelligence with Sequences of Actions - Coordinating Actions in Multi-agent Systems. 181-192
- Rinat Khoussainov, Nicholas Kushmerick: Optimising Performance of Competing Search Engines in Heterogeneous Web Environments. 217-228
- Jianguo Lee, Jingdong Wang, Changshui Zhang: Color Image Segmentation: Kernel Do the Feature Space. 253-264
- Marie-Jeanne Lesot, Florence d'Alché-Buc, George Siolas: Evaluation of Topographic Clustering and Its Kernelization. 265-276
- Yan Liu, Jaime G. Carbonell, Rong Jin: A New Pairwise Ensemble Approach for Text Classification. 277-288
- ShyamSundar Rajaram, Ashutosh Garg, Xiang Sean Zhou, Thomas S. Huang: Classification Approach towards Banking and Sorting Problems. 301-312
- Bohdana Ratitch, Doina Precup: Using MDP Characteristics to Guide Exploration in Reinforcement Learning. 313-324
- Marko Robnik-Sikonja: Experiments with Cost-Sensitive Feature Evaluation. 325-336
- Roberto Santana: A Markov Network Based Factorized Distribution Algorithm for Optimization. 337-348
- Marc Sebban, Henri-Maxime Suchier: On Boosting Improvement: Error Reduction and Convergence Speed-Up. 349-360
- James G. Shanahan, Norbert Roma: Improving SVM Text Classification Performance through Threshold Adjustment. 361-372
- Cynthia A. Thompson, Roger Levy, Christopher D. Manning: A Generative Model for Semantic Role Labeling. 397-408
- Kristina Toutanova, Mark Mitchell, Christopher D. Manning: Optimizing Local Probability Models for Statistical Parsing. 409-420
- Karl Tuyls, Dries Heytens, Ann Nowé, Bernard Manderick: Extended Replicator Dynamics as a Key to Reinforcement Learning in Multi-agent Systems. 421-431
- Jarkko Venna, Samuel Kaski, Jaakko Peltonen: Visualizations for Assessing Convergence and Mixing of MCMC. 432-443
- Ricardo Vilalta, Irina Rish: A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes. 444-455
- Nils Weidmann, Eibe Frank, Bernhard Pfahringer: A Two-Level Learning Method for Generalized Multi-instance Problems. 468-479
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