Statistical Learning With Sparsity: The Lasso and Generalizations [Audiobook] download free by Trevor Hastie

Statistical Learning With Sparsity: The Lasso and Generalizations Audiobook download free by Trevor Hastie
  • Listen audiobook: Statistical Learning With Sparsity: The Lasso and Generalizations
  • Author: Trevor Hastie
  • Release date: 2015/2/3
  • Publisher: TAYLOR & FRANCIS INC
  • Language: English
  • Genre or Collection: Technology and Engineering
  • ISBN: 9781498712163
  • Rating: 7.97 of 10
  • Votes: 551
  • Review by: Charley Phillips
  • Review rating: 9.36 of 10
  • Review Date: 2018/9/7
  • Duration: 4H37M51S in 256 kbps (73.4 MB)
  • Date of creation of the audiobook: 2018-07-17
  • You can listen to this audiobook in formats: MP3, FLAC, WAV, WavPack, TTA, MPEG4, MPEG-4 DST, WMA (compression CAB, ACE, RAR, 7-ZIP, BZ, ZIP)
  • Total pages original book: 367
  • Includes a PDF summary of 37 pages
  • Duration of the summary (audio): 28M17S (7.4 MB)
  • Description or summary of the audiobook: Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of 1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso.In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.
  • Other categories, genre or collection: Probability & Statistics, Automatic Control Engineering, Mathematics, Economic Statistics, Computer Science
  • Download servers: Uploaded, Torrent, 4Shared, iDope, Google Drive, SeedPeer, ZendTo, Dropbox, FreakShare, FileServe. Compressed in CAB, ACE, RAR, 7-ZIP, BZ, ZIP
  • Format: Hardback
  • Approximate value: 99.24 USD
  • Dimensions: 156x235x22.86mm
  • Weight: 816g
  • Printed by: Productivity Press
  • Published in: Portland, United States

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