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Computational Statistics and Machine Learning: A Sparse Approach
Hussain
ISBN: 978-0-470-97356-1
Hardcover
352 pages
July 2015, ©2012
Title in editorial stage
  • Description
This book focuses on using sparse algorithms in statistics and machine learning. The first part addresses the L_0 norm minimization using greedy algorithms and considers the set covering machines, matching pursuit algorithms in machine learning, and random projection methods. The second part, which addresses L_1 norm minimization, discusses linear programming boosting, LASSO/LARS, and compressed sensing. All chapters include a detailed description of algorithms and pseudo-code and, where appropriate, a theoretical analysis of generalization ability motivating the use of sparsity. A final chapter covers applications.
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