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

Buch 28 von 538: Studies in Computational Intelligence
 
9783662507063: Ensembles in Machine Learning Applications (Studies in Computational Intelligence, Band 373)
Softcover
ISBN 10: 3662507064 ISBN 13: 9783662507063
Verlag: Springer, 2016

Inhaltsangabe

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.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Von der hinteren Coverseite

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.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Springer
Erscheinungsdatum
2016
Sprache
Englisch
ISBN 10 
3662507064
ISBN 13 
9783662507063
Einband
Taschenbuch
Anzahl der Seiten
276
Herausgeber
Okun Oleg, Valentini Giorgio, Re Matteo
Kontakt zum Hersteller
ProductSafety@springernature.com
ProductSafety@springernature.com

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Heidelberg
69121
Deutschland

Weitere beliebte Ausgaben desselben Titels

9783642229091: Ensembles in Machine Learning Applications (Studies in Computational Intelligence, 373, Band 373)

Vorgestellte Ausgabe

ISBN 10:  3642229093 ISBN 13:  9783642229091
Verlag: Springer, 2011
Hardcover