Overview
- 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
Part of the book series: Studies in Computational Intelligence (SCI, volume 373)
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Table of contents (14 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Ensembles in Machine Learning Applications
Editors: Oleg Okun, Giorgio Valentini, Matteo Re
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-22910-7
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Berlin Heidelberg 2011
Hardcover ISBN: 978-3-642-22909-1Published: 07 September 2011
Softcover ISBN: 978-3-662-50706-3Published: 23 August 2016
eBook ISBN: 978-3-642-22910-7Published: 01 September 2011
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XX, 252