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Call for Papers ECML/PKDD-2003 http://www.cs.kuleuven.ac.be/conference/ecmlpkdd/ * 14th European Conference on Machine Learning (ECML-2003) * 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-2003) September 22-26, 2003, Cavtat-Dubrovnik, Croatia The 14th European Conference on Machine Learning (ECML) and the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) will be co-located in Cavtat, a small tourist town near Dubrovnik, Croatia, on September 22-26, 2003. Co-ordination of the two conferences provides ample opportunities for cross-fertilization between the two areas, and follows the success of jointly organized ECML/PKDD in 2001 and 2002. Important Dates Submission deadline: Wednesday April 30, 2003 Notification of acceptance: Wednesday June 11, 2003 Camera-ready copies due: Wednesday July 2, 2003 Conferences: Monday September 22 to Friday September 26, 2003 Further details of the submission procedure will be available at the conferences website at http://www.cs.kuleuven.ac.be/conference/ecmlpkdd/ ECML Call for Papers The European Conference on Machine Learning series is intended to provide an international forum for the discussion of the latest high quality research results in machine learning and is the major European scientific event in the field. Submissions are invited that describe empirical and theoretical research in all areas of machine learning. Submissions of papers that describe the application of machine learning methods to real-world problems are encouraged. Topics of interest (non-exhaustive list): abduction, analogy, applications, artificial neural networks, Bayesian networks, case-based reasoning, cognitive modeling, computational learning theory, cooperative learning, decision trees, evolutionary computation, grammatical inference, inductive learning, inductive logic programming, information retrieval and learning, instance based learning, kernel methods, knowledge acquisition and learning, knowledge base refinement, knowledge intensive learning, machine learning of natural language, meta learning, multi-agent learning, multi-strategy learning, pattern recognition, planning and learning, reinforcement learning, revision and restructuring, rule induction, robot learning, discovery of scientific laws, statistical approaches, unsupervised learning, vision and learning. PKDD Call for Papers Data Mining and Knowledge Discovery in Databases (KDD) is a combination of many research areas: databases, statistics, machine learning, automated scientific discovery, artificial intelligence, visualization, decision science, and high performance computing. While each of these areas can contribute in specific ways, KDD focuses on the value that is added by creative combination of the contributing areas. The goal of PKDD is to provide a forum for interaction among all theoreticians and practitioners interested in data mining and KDD. Topics of interest (non-exhaustive list): anytime algorithms, applications, collaborative data mining, database integration, dimensionality reduction, discretization, distributed data mining, incremental algorithms, inductive databases, interactive data mining, knowledge discovery process, multimedia mining, OLAP and data warehouse integration, parallel data mining, personalization and adaptivity, preprocessing and postprocessing, prior knowledge integration, relational data mining, scalable algorithms, scientific discovery, text mining, temporal and spatial data mining, visualization, web mining. Paper Submission There will be a single electronic submission procedure, where authors should indicate whether they submit their paper to ECML, PKDD, or both. In the latter case, the topic of the joint submission must be within the scope of both conferences; accepted joint submissions will be assigned to the more appropriate of the conferences. Student submissions should be clearly indicated on the submission form. All submissions will be reviewed by the respective program committees. The papers should be formatted according to the Springer-Verlag Lecture Notes in Artificial Intelligence guidelines. Authors instructions and style files can be downloaded from http://www.springer.de/comp/lncs/authors.html. The maximum length of papers is 12 pages. The proceedings of both conferences will be published as separate volumes by Springer-Verlag in the Lecture Notes in Artificial Intelligence series and will be available at ECML/PKDD. Simultaneous submissions to other conferences are allowed, provided this fact is clearly indicated on the submission form. Simultaneous submissions that are not clearly specified as such will be rejected. Accepted papers will appear in the ECML/PKDD conference proceedings only if they are withdrawn from proceedings of other conferences. Best Paper Awards KDNet and Kluwer will honour the best (student) papers of ECML and PKDD with awards. The awards will be based on significance and originality of contributions. Sponsors Main sponsor of ECML/PKDD-2003 conferences is KDNet, the FET-OPEN funded European Knowledge Discovery Network of Excellence. ECML/PKDD Program Chairs Nada Lavrac, Jozef Stefan Institute, Ljubljana, Slovenia (ECML/PKDD) Dragan Gamberger, Rudjer Boskovic Institute, Zagreb, Croatia (ECML/PKDD) Hendrik Blockeel, Katholieke Universiteit Leuven, Belgium (ECML) Ljupco Todorovski, Jozef Stefan Institute, Ljubljana, Slovenia (PKDD) For more details, see the conferences website at http://www.cs.kuleuven.ac.be/conference/ecmlpkdd/
