The European Conference on Machine Learning (ECML) and the European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) were jointly organized this year for the ?fth time in a row, after some years of mutual independence before. After Freiburg (2001), Helsinki (2002), Cavtat (2003) and Pisa (2004), Porto received the 16th edition of ECML and the 9th PKDD in October 3-7. Having the two conferences together seems to be working well: 585 di?erent paper submissions were received for both events, which maintains the high s- mission standard of last year. Of these, 335 were submitted to ECML only, 220 to PKDD only and 30 to both. Such a high volume of scienti?c work required a tremendous e?ort from Area Chairs, Program Committee members and some additional reviewers. On average, PC members had 10 papers to evaluate, and Area Chairs had 25 papers to decide upon. We managed to have 3 highly qua- ?edindependentreviewsperpaper(withveryfewexceptions)andoneadditional overall input from one of the Area Chairs. After the authors’ responses and the online discussions for many of the papers, we arrived at the ?nal selection of 40 regular papers for ECML and 35 for PKDD. Besides these, 32 others were accepted as short papers for ECML and 35 for PKDD. This represents a joint acceptance rate of around 13% for regular papers and 25% overall. We thank all involved for all the e?ort with reviewing and selection of papers. Besidesthecoretechnicalprogram, ECMLand PKDDhad6invitedspeakers, 10 workshops, 8 tutorials and a Knowledge Discovery Challenge.
Pavel Brazdil & Rui Camacho
Machine Learning: ECML 2005 [PDF ebook]
16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceedings
Machine Learning: ECML 2005 [PDF ebook]
16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceedings
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Língua Inglês ● Formato PDF ● ISBN 9783540316923 ● Editor Pavel Brazdil & Rui Camacho ● Editora Springer Berlin Heidelberg ● Publicado 2005 ● Carregável 3 vezes ● Moeda EUR ● ID 6316435 ● Proteção contra cópia Adobe DRM
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