The Elements of statistical learning : data mining, inference, and prediction

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Detalles Bibliográficos
Autor principal: Hastie, Trevor
Otros Autores: Tibshirani, Robert, Friedman, Jerome
Formato: Libro
Lenguaje:Inglés
Español
Publicado: New York : Springer, 2017
Edición:2nd ed., 12th repr.
Colección:Springer texts in statistics
Materias:
Aporte de:Registro referencial: Solicitar el recurso aquí
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245 1 4 |a The Elements of statistical learning  |b  : data mining, inference, and prediction  |c  / Trevor Hastie, Robert Tibshirani, Jerome Friedman 
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505 0 |a Introduction -- Overview of supervised learning -- Linear methods for regression -- Linear methods for classification -- Basis expansions and regularization -- Kernel smoothing methods -- 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 nearest neighbors -- Unsupervised learning -- Random forests -- Ensemble learning -- Undirected graphical models -- High-dimensional problems. 
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