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Item Details
Title: ELEMENTS OF STATISTICAL LEARNING
DATA MINING, INFERENCE, AND PREDICTION
Volume: 2017
By: Trevor Hastie, Robert Tibshirani, Jerome Friedman
Format: Hardback

List price: £64.99
Our price: £56.87
Discount:
12.5% off
You save: £8.12
ISBN 10: 0387848576
ISBN 13: 9780387848570
Availability: Usually dispatched within 3-5 days.
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Stock: Currently 31 available
Publisher: SPRINGER-VERLAG NEW YORK INC.
Pub. date: 1 February, 2009
Edition: 2nd Revised edition
Series: Springer Series in Statistics
Pages: 745
Description: 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.
Synopsis: This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 is 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.
Illustrations: 658 black & white illustrations, biography
Publication: US
Imprint: Springer-Verlag New York Inc.
Returns: Returnable
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