Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with SVM
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 ofproblem. In this paper, we used the core idea of multi-objective opti...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | México |
| Institución: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositorio: | Journal of Applied Research and Technology |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs2.localhost:article/147 |
| Acceso en línea: | https://jart.icat.unam.mx/index.php/jart/article/view/147 |
| Access Level: | acceso abierto |
| Palabra clave: | MOPSO-CD SVM fingerprint recognition |
| Sumario: | 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 ofproblem. In this paper, we used the core idea of multi-objective optimization to transform SVM into a new form. Thisform of SVM could help to solve the situation: in tradition, SVM is usually a single optimization equation, andparameters for this algorithm can only be determined by user’s experience, such as penalty parameter. Therefore, ouralgorithm is developed to help user prevent from suffering to use this algorithm in the above condition. We use multiobjectiveParticle Swarm Optimization algorithm in our research and successfully proved that user do not need to usetrial – and – error method to determine penalty parameter C. Finally, we apply it to NIST-4 database to assess ourproposed algorithm feasibility, and the experiment results shows our method can have great results as we expect. |
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