Algorithms for Sparsity-Constrained Optimization
| Autor Principal: | |
|---|---|
| Formato: | Libro |
| Lengua: | inglés |
| Datos de publicación: |
Cham :
Springer International Publishing : Imprint: Springer,
2014.
|
| Series: | Springer Theses, Recognizing Outstanding Ph.D. Research,
261 |
| Temas: | |
| Acceso en línea: | http://dx.doi.org/10.1007/978-3-319-01881-2 |
| Resumen: | 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. |
| Descripción Física: | xxiI, 107 p : il. |
| ISBN: | 9783319018812 |
| ISSN: | 2190-5053 ; |
| DOI: | 10.1007/978-3-319-01881-2 |