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statistical learning sparsity

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Author: Trevor Hastie, Martin Wainwright Edition: 1 edition Publication Date: May 7, 2015 Publisher: Chapman and Hall/CRC ISBN-10: 1498712169 ISBN-13: 978-1498712163 Pages: 367 pages Format: pdf Size: 9.3M --- A 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 ℓ1 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. --- http://dlfeb.com/book/statistical-learning-with-sparsity-lasso-and-generalizations-chapman-hall-crc-monographs-on-statistics-applied-probability/

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dlfeb.com.Statistical.Learning.with.Sparsity.The.Lasso.and.Generalizations.Chapman.Hall.CRC.Monographs.on.Statistics.Applied.Probability.pdf 9.2 MB

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statistical learning sparsity

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