Sparse Modeling

Theory, Algorithms, and Applications

Irina Rish, Genady Grabarnik

$158

Hardback

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English

CRC Press Inc
01 December 2014

Databases; Machine learning

Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition

Sparse modeling is an important issue in many applications of machine learning and statistics where the main objective is discovering predictive patterns in data to enhance understanding of underlying physical, biological, and other natural processes. This book surveys recent advances in statistics, machine learning, and signal processing related to sparse modeling. It provides a comprehensive introduction to recent developments in sparse modeling research, including the theoretical basis for sparse modeling, algorithmic approaches, and applications to computational biology, medicine, neuroscience, graphical model selection, and compressed sensing.

By: Irina Rish, Genady Grabarnik
Imprint: CRC Press Inc
Country of Publication: United States
Dimensions: Height: 234mm, Width: 156mm, Spine: 18mm
Weight: 800g
ISBN: 9781439828694
ISBN 10: 1439828695
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition
Pages: 254
Publication Date: 01 December 2014
Audience: Professional and scholarly , College/higher education , Undergraduate , Primary
Format: Hardback
Publisher's Status: Active

Irina Rish, Genady Grabarnik

Reviews for Sparse Modeling: Theory, Algorithms, and Applications

... 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 - Ecole Normale Superieure, Paris