Computational load reduction in decision functions using support vector machines
| Autor: | Saturnino Maldonado-Bascón, Javier Acevedo-Rodríguez, Philip Siegmann, S. Lafuente-Arroyo, Francisco López-Ferreras |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Decision support system
Computational complexity theory business.industry Evaluation function Machine learning computer.software_genre Support vector machine Kernel method Control and Systems Engineering Kernel (statistics) Signal Processing Radial basis function kernel Margin classifier Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering business Algorithm computer Software Mathematics |
| Zdroj: | Signal Processing. 89:2066-2071 |
| ISSN: | 0165-1684 |
| DOI: | 10.1016/j.sigpro.2009.03.032 |
| Popis: | A new method of reducing the computational load in decision functions provided by a support vector classification machine is studied. The method exploits the geometrical relations when the kernels used are based on distances to obtain bounds of the remaining decision function and avoids to continue calculating kernel operations when there is no chance to change the decision. The method proposed achieves savings in operations of 25-90% whilst keeping the same accuracy. Although the method is explained for support vector machines, it can be applied to any kernel binary classifier that provides a similar evaluation function. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect