This book constitutes the refereed proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with ECML 2003.The 40 revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another 40 ones for ECML 2003, selected from a total of 332 submissions. The papers address all current issues in data mining and knowledge discovery in databases including data mining tools, association rule mining, classification, clustering, pattern mining, multi-relational classifiers, boosting, kernel methods, learning Bayesian networks, inductive logic programming, user preferences mining, time series analysis, multi-view learning, support vector machine, pattern mining, relational learning, categorization, information extraction, decision making, prediction, and decision trees.
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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).
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Includes supplementary material: sn.pub/extras
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Produktdetaljer

ISBN
9783540200857
Publisert
2003-09-11
Utgiver
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, G, P, U, 01, 06, 05
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
16

Biografisk notat

This book constitutes the refereed proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with ECML 2003. The 40 revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another 40 ones for ECML 2003, selected from a total of 332 submissions. The papers address all current issues in data mining and knowledge discovery in databases including data mining tools, association rule mining, classification, clustering, pattern mining, multi-relational classifiers, boosting, kernel methods, learning Bayesian networks, inductive logic programming, user preferences mining, time series analysis, multi-view learning, support vector machine, pattern mining, relational learning, categorization, information extraction, decision making, prediction, and decision trees.