The elements of statistical learning : data mining, inference, and prediction
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the fiel...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Livre numérique |
| Sprache: | Anglais |
| Veröffentlicht: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Ausgabe: | 2nd edition. |
| Schriftenreihe: | Springer Series in Statistics
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| Schlagworte: | |
| Online Zugang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Anmerkung: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • The elements of statistical learning, data mining, inference, and prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2nd edition, corrected at 5th printing, 2011, New York (N.Y.), Springer, 1 vol. (XXII- 745 p.), Springer series in statistics, 978-0-387-84857-0 • The Elements of Statistical Learning, Texte imprimé, 9780387848846 • The Elements of Statistical Learning, Texte imprimé, 9780387848570 • The Elements of Statistical Learning, Texte imprimé, 9781071621226 |
Inhaltsangabe:
- 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: p ? N