Ubicación Física: 006.31 / H356
The elements of statistical learning : data mining, inference, and prediction / | |
Autor: | Hastie, Trevor. |
Otros Autores: | Friedman, Jerome ; Tibshirani, Robert. |
Serie: | Springer series in statistics. |
Pié de imprenta: | New York : Springer, 2009. |
Edición: | 2a. ed. |
Descripción: | 745 p. il. |
ISBN: | 9780387848570. |
Tema(s): | |
Contenido: | Introduction. Overview of supervised learning. Linear methods for regression. Linear methods for classification. Basis expansions and regularization. Model assessment and selection. Model inference and averaging. Additive models, trees, and related methods. Boosting and additive trees. Neural networks. Support vector machines and flexible discriminants. Prototype methods and neares-neighbors. Unsupervised learning. Random forests. Ensemble learning. Undirected graphical models. High-dimensional problems. |
Tipo de ítem | Biblioteca actual | Colección | Signatura topográfica | Copia número | Estado | Fecha de vencimiento | Código de barras | |
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Libro - General | BIBLIOTECA SEDE LA CARO Depósito | Colección General | 006.31 / H356 (Navegar estantería(Abre debajo)) | Ej. 1 | Disponible | 50156 | ||
Libro - General | BIBLIOTECA SEDE LA CARO Depósito | Colección General | 006.31 / H356 (Navegar estantería(Abre debajo)) | Ej. 2 | Disponible | 50157 |
Introduction. Overview of supervised learning. Linear methods for regression. Linear methods for classification. Basis expansions and regularization. Model assessment and selection. Model inference and averaging. Additive models, trees, and related methods. Boosting and additive trees. Neural networks. Support vector machines and flexible discriminants. Prototype methods and neares-neighbors. Unsupervised learning. Random forests. Ensemble learning. Undirected graphical models. High-dimensional problems.
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