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Introduction to ensemble methods /
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After pr...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, Fla. ; London :
Chapman & Hall/CRC,
2012.
|
| Colección: | Chapman & Hall/CRC data mining and knowledge discovery series.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Zhou, Zhi-Hua, |c Ph. D., |e author. | |
| 245 | 1 | 0 | |a Introduction to ensemble methods / |c by Zhi-Hua Zhou. |
| 264 | 1 | |a Boca Raton, Fla. ; |a London : |b Chapman & Hall/CRC, |c 2012. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Chapman & Hall/CRC data mining and knowledge discovery series | |
| 504 | |a Includes bibliographical references. | ||
| 520 | |a An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement. | ||
| 588 | |a Description based on print version record. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning |x Mathematics. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 2 | |a Algorithms | |
| 650 | 6 | |a Apprentissage automatique |x Mathématiques. | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 7 | |a algorithms. |2 aat | |
| 650 | 7 | |a Algorithms |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Zhou, Zhi-Hua, Ph. D. |t Introduction to ensemble methods. |d Boca Raton, Fla. ; London : Chapman & Hall/CRC, 2012 |z 9781439830031 |w (OCoLC)760291251 |
| 830 | 0 | |a Chapman & Hall/CRC data mining and knowledge discovery series. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781439830055/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||