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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.
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.
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
Classification.- Bias-Variance Analysis of ECOC and Bagging Using Neural Nets.- Fast-ensembles of Minimum Redundancy Feature Selection.
| Erscheinungsjahr: | 2016 |
|---|---|
| Fachbereich: | Technik allgemein |
| Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xx
252 S. |
| ISBN-13: | 9783662507063 |
| ISBN-10: | 3662507064 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Okun, Oleg
Valentini, Giorgio Re, Matteo |
| Redaktion: |
Okun, Oleg
Re, Matteo Valentini, Giorgio |
| Herausgeber: | Oleg Okun/Giorgio Valentini/Matteo Re |
| Auflage: | Softcover reprint of the original 1st edition 2011 |
| Hersteller: |
Springer
Springer-Verlag GmbH Springer Berlin Heidelberg |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 16 mm |
| Von/Mit: | Oleg Okun (u. a.) |
| Erscheinungsdatum: | 23.08.2016 |
| Gewicht: | 0,423 kg |
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