| Título: | Support Vector Machine Optimized by Improved Genetic Algorithm |
| Autores: |
Sheng, Xiang Chang; Hunan Institute of Engineering Yu, Zhou; Henan Institute of Science and Technology Qu, Xilong; Hunan Institute of Engineering |
| Fecha: | 2013-07-22 |
| Publicador: | TELKOMNIKA: Indonesian journal of electrical engineering |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| Tema: |
Technology support vector machine; genetic algorithm; parameter optimization; cross-validation |
| Descripción: | Parameters of support vector machines (SVM) which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation show that the proposed method not only can assure the classification precision, but also can reduce training time markedly compared with standard genetic algorithm. |
| Idioma: | Inglés |