The Elements of Statistical Learning. Data Mining, Inference, and Prediction
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. I...
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| Autors principals: | , , |
|---|---|
| Format: | Electrònic Llibre |
| Idioma: | anglès |
| Publicat: |
New York, USA:
Springer Science+Business Media,
2009.
|
| Edició: | Second edition. |
| Col·lecció: | Springer Series in Statistics (SSS).
|
| Matèries: | |
| Accés en línia: | Disponible Online. |
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| 020 | |a 978-0-387-84857-0 (Hardcover) | ||
| 020 | |a 978-0-387-84858-7 (eBook) | ||
| 024 | |2 DOI |a 10.1007/978-0-387-84858-7 | ||
| 040 | |c Pontificia Universidad Católica de Valparaíso | ||
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| 100 | |a Hastie, Trevor J |e author |9 222215 | ||
| 245 | 1 | 4 | |a The Elements of Statistical Learning. |b Data Mining, Inference, and Prediction |h [electronic resource]. |
| 250 | |a Second edition. | ||
| 260 | |a New York, USA: |b Springer Science+Business Media, |c 2009. | ||
| 300 | |a xxii, 745 páginas: ilustraciones. |b Online resource. | ||
| 490 | |a Springer Series in Statistics (SSS). | ||
| 520 | |a This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates. | ||
| 583 | |a last modification |c 20151027 02:51:44 |k Rodrigo Vera | ||
| 650 | 0 | |a Estadística |9 259535 |x Biología, finanzas, marketing y medicina |v Análisis conceptual | |
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| 700 | 0 | |a Tibshirani, Robert |e author |9 569152 | |
| 700 | 0 | |a Friedman, Jerome |e author |9 569153 | |
| 856 | |u https://doi.org/10.1007/978-0-387-84858-7 |y Disponible Online. | ||
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