Machine Learning: ECML 2003

Prof. Dr. Nada Lavrac heads the Department of Knowledge Technologies at the Jo ef Stefan Institute in Ljubljana. She is the author and editor of several books and proceedings in the field of data mining and machine learning, and she has chaired or served on the boards of the main related journals and conferences. Her research interests include machine learning, data mining, and inductive logic programming, and related applications in medicine, public health, bioinformatics, and the management of virtual enterprises. In 1997 she was awarded the Ambassador of Science of Slovenia prize, and in 2007 she was elected as an ECCAI Fellow.

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: AFast 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.