Ensembles in Machine Learning Applications

Language: English

Published by Springer, Springer Sep 2011, 2011

3642229093 / 9783642229091

Series: Book 28 of 538 - Studies in Computational Intelligence

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This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 272 pp. Englisch.

Seller Inventory # 9783642229091

Title
Ensembles in Machine Learning Applications
Author
Oleg Okun
Publisher
Springer, Springer Sep 2011
Publication year
2011
Condition
Neu
Binding
Buch
Language
English
ISBN 10
3642229093
ISBN 13
9783642229091
Item weight
626 grams
Dimensions
241x160x22 mm
Series
Book 28 of 538: Studies in Computational Intelligence

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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