The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition download free [PDF and Ebook] by Trevor Hastie

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition download free [PDF and Ebook] by Trevor Hastie year 2009
  • Book name: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
  • Author: Trevor Hastie
  • Release date: 2009/9/20
  • Publisher: SPRINGER-VERLAG NEW YORK INC.
  • Language: English
  • Genre or Collection: Computing
  • ISBN: 9780387848570
  • Rating: 8 of 10
  • Votes: 62
  • Review by: Estelle Berlin
  • Review rating: 8.34 of 10
  • Review Date: 2018/11/30
  • Total pages: 745
  • Includes a PDF summary of 65 pages
  • Description or summary of the book: 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.
  • Estimated reading time (average reader): 33H43M54S
  • Other categories, genre or collection: Stochastics, Computing: General, Life Sciences: General Issues, Artificial Intelligence, Digital TV & Media Centres: Consumer/user Guides, Biology, Life Sciences, Data Mining, Probability & Statistics, Expert Systems / Knowledge-based Systems, Computer Science
  • Available formats: RTF, PDF, HTML, TXT, EPUB, WORD, DOC, AZW. Compressed in ZIP, CBC, CHM, RAR, BZ2
  • Download servers: Mediafire, Waistra, Koofr, Uploaded, Hotfile, Torrent, FileServe, Microsoft OneDrive
  • Format: Hardback
  • Approximate value: 89.21 USD
  • Dimensions: 155x235x38.1mm
  • Weight: 1,383.46g
  • Printed by: Not Available
  • Published in: New York, NY, United States
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