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.
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Algorithms for Sparsity-Constrained Optimization
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.
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Algorithms for Sparsity-Constrained Optimization
107Algorithms for Sparsity-Constrained Optimization
107Paperback(Softcover reprint of the original 1st ed. 2014)
$169.99
169.99
In Stock
Product Details
ISBN-13: | 9783319377193 |
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Publisher: | Springer International Publishing |
Publication date: | 08/23/2016 |
Series: | Springer Theses , #261 |
Edition description: | Softcover reprint of the original 1st ed. 2014 |
Pages: | 107 |
Product dimensions: | 6.10(w) x 9.25(h) x (d) |
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