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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 o...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2014.
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| Edición: | 1st ed. 2014. |
| Colección: | Springer Theses, Recognizing Outstanding Ph.D. Research,
261 |
| Temas: | |
| Acceso en línea: | Texto Completo |
| Sumario: | 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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| Descripción Física: | XXI, 107 p. 13 illus., 12 illus. in color. online resource. |
| ISBN: | 9783319018812 |
| ISSN: | 2190-5061 ; |