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Computational Statistics and Machine Learning : A Sparse Approach, Hardback Book

Computational Statistics and Machine Learning : A Sparse Approach Hardback

Part of the Wiley Series in Probability and Statistics series

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Computational Statistics and Machine Learning: A Sparse Approach 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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