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Sparse Modeling: Theory, Algorithms, and Applications (Chapman & Hall/CRC Machine Learning & Pattern Recognition) Hardcover – 17 Nov 2014

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  • Sparse Modeling: Theory, Algorithms, and Applications (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
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  • Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
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"… an excellent introductory book for branching off into aspects of sparse modeling; it is also good for advanced students since it is contains an appendix with some of the mathematical background needed to learn from this book, including topics such as eigentheory, discrete Fourier transform, and subgaussian random variables. I very much recommend this book for researchers and students alike."
Computing Reviews, May 2015

"A comprehensive, clear, and well-articulated book on sparse modeling. This book will stand as a prime reference to the research community for many years to come."
―Ricardo Vilalta, Department of Computer Science, University of Houston

"This book provides a modern introduction to sparse methods for machine learning and signal processing, with a comprehensive treatment of both theory and algorithms. Sparse Modeling is an ideal book for a first-year graduate course."
―Francis Bach, INRIA - École Normale Supérieure, Paris

About the Author

Irina Rish is a Research Staff Member at IBM T. J. Watson Research Center, New York.


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Amazon.com: 5.0 out of 5 stars 1 review
0 of 1 people found the following review helpful
5.0 out of 5 stars Five Stars 7 Mar. 2016
By I. Rosen - Published on Amazon.com
Format: Hardcover Verified Purchase
Excellent review of an important area in compressive sensing and machine learning.
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