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Tipo: materialTypeLabelLibro - General
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.

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Libro - General Libro - General Biblioteca UCATOLICA
Carrera 13
Colección General 006.31 / H356 (Browse shelf) Ej. 1 Available (Acceso Libre) 50156
Libro - General Libro - General Biblioteca UCATOLICA
Carrera 13
Colección General 006.31 / H356 (Browse shelf) Ej. 2 Available (Acceso Libre) 50157
Browsing Biblioteca UCATOLICA Shelves , Shelving location: Carrera 13 , Collection code: Colección General Close shelf browser
006.31 / G377 Hands-on machine learning with scikit-learn and tensorFlow : 006.31 / G651 Depp learning / 006.31 / H356 The elements of statistical learning : 006.31 / H356 The elements of statistical learning : 006.312 / A266 Data mining : 006.312 / P969 Data science for business : 006.312/S558/2018 Data mining for business analytics :

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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