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.
| ISBN: | 9783319377193 |
| Publication date: | 23rd August 2016 |
| Author: | Sohail Bahmani |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Paperback |
| Pagination: | 107 pages |
| Series: | Springer Theses |
| Genres: |
Electronics engineering Computer vision Digital signal processing (DSP) Mathematical theory of computation |
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.
Algorithms for Sparsity-Constrained Optimization features in the following genres: Electronics engineering, Computer vision, Digital signal processing (DSP), Mathematical theory of computation
Algorithms for Sparsity-Constrained Optimization is available in Paperback, Hardback
Algorithms for Sparsity-Constrained Optimization was written by Sohail Bahmani and published by Springer an imprint of Springer International Publishing
Algorithms for Sparsity-Constrained Optimization has 107 pages
Yes it is part of Springer Theses series