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
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Algorithms for Sparsity-Constrained Optimization
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Product Details
| ISBN-13: | 9783319018812 |
|---|---|
| Publisher: | Springer-Verlag New York, LLC |
| Publication date: | 10/07/2013 |
| Series: | Springer Theses , #261 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 107 |
| File size: | 3 MB |
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