Studies in Computational Intelligence

Ensembles in Machine Learning Applications

Editors: Okun, Oleg, Valentini, Giorgio, Re, Matteo (Eds.)

  • 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
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About this book

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.

Table of contents (14 chapters)

  • Facial Action Unit Recognition Using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classifiers

    Smith, Raymond S. (et al.)

    Pages 1-20

  • On the Design of Low Redundancy Error-Correcting Output Codes

    Bautista, Miguel Ángel (et al.)

    Pages 21-38

  • Minimally-Sized Balanced Decomposition Schemes for Multi-class Classification

    Smirnov, Evgueni N. (et al.)

    Pages 39-58

  • Bias-Variance Analysis of ECOC and Bagging Using Neural Nets

    Zor, Cemre (et al.)

    Pages 59-73

  • Fast-Ensembles of Minimum Redundancy Feature Selection

    Schowe, Benjamin (et al.)

    Pages 75-95

Buy this book

eBook $139.00
price for USA (gross)
  • ISBN 978-3-642-22910-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $179.00
price for USA
  • ISBN 978-3-642-22909-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Ensembles in Machine Learning Applications
Editors
  • Oleg Okun
  • Giorgio Valentini
  • Matteo Re
Series Title
Studies in Computational Intelligence
Series Volume
373
Copyright
2011
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer Berlin Heidelberg
eBook ISBN
978-3-642-22910-7
DOI
10.1007/978-3-642-22910-7
Hardcover ISBN
978-3-642-22909-1
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XX, 252
Topics