Machine Learning: ECML 2003

The proceedings of ECML/PKDD2003 are published in two volumes: the P- ceedings of the 14th European Conference on Machine Learning (LNAI 2837) and the Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (LNAI 2838). The two conferences were held on September 22-26, 2003 in Cavtat, a small tourist town in the vicinity of Dubrovnik, Croatia. As machine learning and knowledge discovery are two highly related ?elds, theco-locationofbothconferencesisbene?cialforbothresearchcommunities.In Cavtat, ECML and PKDD were co-located for the third time in a row, following the successful co-location of the two European conferences in Freiburg (2001) and Helsinki (2002). The co-location of ECML2003 and PKDD2003 resulted in a joint program for the two conferences, including paper presentations, invited talks, tutorials, and workshops. Out of 332 submitted papers, 40 were accepted for publication in the ECML2003proceedings,and40wereacceptedforpublicationinthePKDD2003 proceedings. All the submitted papers were reviewed by three referees. In ad- tion to submitted papers, the conference program consisted of four invited talks, four tutorials, seven workshops, two tutorials combined with a workshop, and a discovery challenge.

Inhalt

Invited Papers.- From Knowledge-Based to Skill-Based Systems: Sailing as a Machine Learning Challenge.- Two-Eyed Algorithms and Problems.- Next Generation Data Mining Tools: Power Laws and Self-similarity for Graphs, Streams and Traditional Data.- Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketing Interventions.- Contributed Papers.- Support Vector Machines with Example Dependent Costs.- Abalearn: A Risk-Sensitive Approach to Self-play Learning in Abalone.- Life Cycle Modeling of News Events Using Aging Theory.- Unambiguous Automata Inference by Means of State-Merging Methods.- Could Active Perception Aid Navigation of Partially Observable Grid Worlds?.- Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language.- Iteratively Extending Time Horizon Reinforcement Learning.- Volume under the ROC Surface for Multi-class Problems.- Improving the AUC of Probabilistic Estimation Trees.- Scaled CGEM: A Fast Accelerated EM.- Pairwise Preference Learning and Ranking.- A New Way to Introduce Knowledge into Reinforcement Learning.- Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference.- COllective INtelligence with Sequences of Actions.- Rademacher Penalization over Decision Tree Prunings.- Learning Rules to Improve a Machine Translation System.- Optimising Performance of Competing Search Engines in Heterogeneous Web Environments.- Robust k-DNF Learning via Inductive Belief Merging.- Logistic Model Trees.- Color Image Segmentation: Kernel Do the Feature Space.- Evaluation of Topographic Clustering and Its Kernelization.- A New Pairwise Ensemble Approach for Text Classification.- Self-evaluated Learning Agent in Multiple State Games.- Classification Approach towards Ranking and Sorting Problems.- Using MDP Characteristics to Guide Exploration in Reinforcement Learning.- Experiments with Cost-Sensitive Feature Evaluation.- A Markov Network Based Factorized Distribution Algorithm for Optimization.- On Boosting Improvement: Error Reduction and Convergence Speed-Up.- Improving SVM Text Classification Performance through Threshold Adjustment.- Backoff Parameter Estimation for the DOP Model.- Improving Numerical Prediction with Qualitative Constraints.- A Generative Model for Semantic Role Labeling.- Optimizing Local Probability Models for Statistical Parsing.- Extended Replicator Dynamics as a Key to Reinforcement Learning in Multi-agent Systems.- Visualizations for Assessing Convergence and Mixing of MCMC.- A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes.- Improving Rocchio with Weakly Supervised Clustering.- A Two-Level Learning Method for Generalized Multi-instance Problems.- Clustering in Knowledge Embedded Space.- Ensembles of Multi-instance Learners.