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9783662507063: Ensembles in Machine Learning Applications

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ISBN 10 : 3662507064 ISBN 13 : 9783662507063

Editeur : Springer, 2016

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This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods
and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain).
As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms – advanced machine
learning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a group
of algorithms, each of which first independently solves the task at hand by assigning a class or cluster label
(voting) to instances in a dataset and after that all votes are combined together to produce the final class or
cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems.

This book consists of 14 chapters, each of which can be read independently of the others. In addition to two
previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or
programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in
practice and to help to both researchers and engineers developing ensemble applications.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer
Date d'�dition: 2016
Langue: anglais
ISBN 10: 3662507064
ISBN 13: 9783662507063
Reliure: Broch�
Nombre de pages: 276
�diteur: Okun Oleg, Valentini Giorgio, Re Matteo
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Ensembles in Machine Learning Applications

Okun, Oleg|Valentini, Giorgio|Re, Matteo

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ISBN 10 : 3662507064 ISBN 13 : 9783662507063

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Ensembles in Machine Learning Applications

Oleg Okun

Edit� par Springer Berlin Heidelberg, 2016

ISBN 10 : 3662507064 ISBN 13 : 9783662507063

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain). As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms - advanced machinelearning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a groupof algorithms, each of which first independently solves the task at hand by assigning a class or cluster label (voting) to instances in a dataset and after that all votes are combined together to produce the final class or cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems. This book consists of 14 chapters, each of which can be read independently of the others. In addition to two previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in practice and to help to both researchers and engineers developing ensemble applications. N� de r�f. du vendeur 9783662507063

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Ensembles in Machine Learning Applications

Oleg Okun

Edit� par Springer Berlin Heidelberg Aug 2016, 2016

ISBN 10 : 3662507064 ISBN 13 : 9783662507063

Neuf Taschenbuch

impression � la demande

Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain). As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms - advanced machinelearning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a groupof algorithms, each of which first independently solves the task at hand by assigning a class or cluster label (voting) to instances in a dataset and after that all votes are combined together to produce the final class or cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems. This book consists of 14 chapters, each of which can be read independently of the others. In addition to two previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in practice and to help to both researchers and engineers developing ensemble applications. 276 pp. Englisch. N� de r�f. du vendeur 9783662507063

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Ensembles in Machine Learning Applications (Studies in Computational Intelligence, 373)

Edit� par Springer, 2016

ISBN 10 : 3662507064 ISBN 13 : 9783662507063

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Ensembles in Machine Learning Applications

Oleg Okun

Edit� par Springer Berlin Heidelberg, Springer Berlin Heidelberg Aug 2016, 2016

ISBN 10 : 3662507064 ISBN 13 : 9783662507063

Neuf Taschenbuch

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. Neuware -This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methodsand their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning andPrinciples and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain).As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms � advanced machinelearning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a groupof algorithms, each of which first independently solves the task at hand by assigning a class or cluster label(voting) to instances in a dataset and after that all votes are combined together to produce the final class orcluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems.This book consists of 14 chapters, each of which can be read independently of the others. In addition to twoprevious SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/orprogramming code of the algorithms described in them. This was done in order to facilitate ensemble adoption inpractice and to help to both researchers and engineers developing ensemble applications.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 276 pp. Englisch. N� de r�f. du vendeur 9783662507063

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