The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Material type:
TextPublication details: New YORK: Springer, 2017Description: xxii, 745pISBN: 9780387848570Subject(s): Artificial intelligence Bioinformatics Data mining | Electronic data processingDDC classification: 006.3122
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.
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
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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