The Elements of Statistical Learning: Data Mining, Inference, and Prediction

By:

Hastie, Trevor

Contributor(s):

Material type:

TextPublication details: New YORK: Springer, 2017Description: xxii, 745pISBN:

9780387848570

Subject(s):

DDC classification:

2nd ed. 006.3122 HAS/E

Contents:

Contenido: 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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Holdings
Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	General Books	Central Library, Sikkim University General Book Section				006.3122 HAS/E (Browse shelf(Opens below))					Available			050571

Total holds: 0

Contenido: 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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