| Revista: | Journal of applied research and technology |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000380486 |
| ISSN: | 1665-6423 |
| Autores: | Hsieh, Ching-Tang1 Hu, Chia-Shing1 |
| Instituciones: | 1Tamkang University, Department of Electrical Engineering, Taipei. Taiwán |
| Año: | 2014 |
| Periodo: | Dic |
| Volumen: | 12 |
| Número: | 6 |
| Paginación: | 1014-1024 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | Researchers put efforts to discover more efficient ways to classification problems for a period of time. Recent years, the support vector machine (SVM) becomes a well-popular intelligence algorithm developed for dealing this kind of problem. In this paper, we used the core idea of multi-objective optimization to transform SVM into a new form. This form of SVM could help to solve the situation: in tradition, SVM is usually a single optimization equation, and parameters for this algorithm can only be determined by user's experience, such as penalty parameter. Therefore, our algorithm is developed to help user prevent from suffering to use this algorithm in the above condition. We use multi- objective Particle Swarm Optimization algorithm in our research and successfully proved that user do not need to use trial – and – error method to determine penalty parameter C. Finally, we apply it to NIST-4 database to assess our proposed algorithm feasibility, and the experiment results shows our method can have great results as we expect |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Programación, Huellas dactilares, Reconocimiento, Optimización multiobjetivo, Algoritmos |
| Keyword: | Computer science, Programming, Fingerprints, Recognition, Multiobjective optimizing, Algorithms |
| Texto completo: | Texto completo (Ver HTML) |
