Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases

EnglishPaperback / softbackPrint on demand
Springer, Berlin
EAN: 9783642041730
Print on demand
Delivery on Friday, 13. of December 2024
CZK 2,633
Common price CZK 2,925
Discount 10%
pc
Do you want this product today?
Oxford Bookshop Praha Korunní
not available
Librairie Francophone Praha Štěpánská
not available
Oxford Bookshop Ostrava
not available
Oxford Bookshop Olomouc
not available
Oxford Bookshop Plzeň
not available
Oxford Bookshop Brno
not available
Oxford Bookshop Hradec Králové
not available
Oxford Bookshop České Budějovice
not available
Oxford Bookshop Liberec
not available

Detailed information

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
EAN 9783642041730
ISBN 3642041736
Binding Paperback / softback
Publisher Springer, Berlin
Publication date September 3, 2009
Pages 762
Language English
Dimensions 235 x 155
Country Germany
Readership Professional & Scholarly
Illustrations XXIX, 762 p.
Editors Buntine Wray; Grobelnik Marko; Mladenic Dunja; Shawe-Taylor John
Series Lecture Notes in Artificial Intelligence