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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) Book
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Foyles
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketingĀ in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It isĀ a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.
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BookDepository
The Elements of Statistical Learning : Hardback : Springer-Verlag New York Inc. : 9780387848570 : 0387848576 : 21 Apr 2017 : 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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Blackwell
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...
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Pickabook
Trevor Hastie, Robert Tibshirani, Jerome Friedman
- 0387848576
- 9780387848570
- Trevor Hastie, Robert Tibshirani, Jerome Friedman
- 9 February 2009
- Springer
- Hardcover (Book)
- 746
- 2nd ed. 2009. Corr. 3rd printing
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