Algorithms for Sparsity-Constrained Optimization: 261 (Springer Theses, 261) - Image 1

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Springer Algorithms for Sparsity-Constrained Optimization: 261 (Springer Theses, 261)

Name: Algorithms for Sparsity-Constrained Optimization: 261 (Springer Theses, 261)
Brand: Springer
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Product Description This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a "greedy" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use of previous models. From the Back Cover This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a"greedy" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use of previous models. About the Author Dr. Bahmani completed his thesis at Carnegie Mellon University and is currently employed by the Georgia Institute of Technology.

Product Specifications

Brand: Springer
Format: hardcover
ASIN: 3319018809
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 18 October 2013
Listed Since: 25 July 2013

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Springer Algorithms for Sparsity-Constrained Optimization: 261 (Springer Theses, 261)

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