Ensembles in machine learning applications. Studies in computational intelligence; Vol. 373.

Okun, Oleg [Hrsg.], Giorgio [Hrsg.] Valentini and Matteo [Hrsg.] Re:

ISBN 10: 3642229093 / ISBN 13: 9783642229091

Editore: Berlin ; Heidelberg : Springer, 2011

Riguardo questo articolo

XX, 252 S.: Ill., graph. Darst. ; 24 cm, Einband berieben. ISBN: 9783642229091 Sprache: Englisch Gewicht in Gramm: 680. Codice articolo 27128

Dati bibliografici

Titolo: Ensembles in machine learning applications. ...

Casa editrice: Berlin ; Heidelberg : Springer

Data di pubblicazione: 2011

Legatura: OPp., gebundene Ausgabe.

Condizione: Befriedigend

Descrizione articolo

Riassunto:

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.

Contenuti:

From the content: Facial Action Unit Recognition Using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classifiers.- On the Design of Low Redundancy Error-Correcting Output Codes.- Minimally-Sized Balanced Decomposition Schemes for Multi-Class Classification.- Bias-Variance Analysis of ECOC and Bagging Using Neural Nets.- Fast-ensembles of Minimum Redundancy Feature Selection.