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Computational Statistics and Machine Learning: A Sparse Approach
By
John Shawe-Taylor and
Zakria Hussain
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Bibliographic Detail
Publisher
John Wiley & Sons Inc
Publication date
September 13, 2016
Pages
352
Binding
Hardcover
Book category
Adult Non-Fiction
ISBN-13
9780470973561
ISBN-10
0470973560
Dimensions
0 by 5 by 9 in.
Original list price
$99.95
Summaries and Reviews
Amazon.com description: Product Description: 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.
Editions
Hardcover
The price comparison is for this edition
With John Shawe-Taylor |
from John Wiley & Sons Inc (September 13, 2016)
9780470973561 | details & prices | 352 pages | List price $99.95
About: Computational Statistics and Machine Learning: A Sparse Approach focuses on using sparse algorithms in statistics and machine learning.
About: Computational Statistics and Machine Learning: A Sparse Approach focuses on using sparse algorithms in statistics and machine learning.
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