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Okun / Valentini / Re

Ensembles in Machine Learning Applications

2011. Buch. XX, 252 S.: 50 s/w-Abbildungen, 28 Farbabbildungen. Hardcover
Springer ISBN 978-3-642-22909-1
Gewicht: 622 g
In englischer Sprache
Das Werk ist Teil der Reihe:
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.
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Recent research on Ensembles in Machine Learning Applications  Edited outcome of the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications held in Barcelona on September 20, 2010 Written by leading experts in the field
Herausgegeben von: link iconOleg Okun, link iconGiorgio Valentini und link iconMatteo Re