The elements of statistical learning: data mining, inference, and prediction.
By: Hastie, Trevor
Contributor(s): Tibshirani, Robert | Friedman, Jerome
Language: English Publisher: United States of America -- Springer -- 2009Edition: 2nd edDescription: xxii, 745pISBN: 9780387848570Subject(s): Computer science | Artificial Intelligence | Machine learning | Bioinformatics | Forecasting | Computational intelligenceDDC classification: 006.3 HAS/E Summary:
This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
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Central Library Reference (Sahyadri Campus) | Reference | 006.3 HAS/E | Not for loan | 08076 | |
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Central Library General Stack (Nila Campus) | 006.3 HAS/E | Available | 08081 | ||
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Central Library General Stack (Nila Campus) | 006.3 HAS/E | Available | 08077 | ||
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Central Library General Stack (Nila Campus) | 006.3 HAS/E | Checked out | 18/09/2024 | 08080 | |
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Central Library General Stack (Nila Campus) | 006.3 HAS/E | Available | 08079 | ||
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Central Library General Stack (Nila Campus) | 006.3 HAS/E | Available | 08078 |
* 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
This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.