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Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with SVM
ARTICLE

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Journal of Applied Research and Technology Volume 12, Number 6, ISSN 1665-6423 Publisher: Elsevier Ltd

Abstract

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.

Citation

Hsieh, C.T. & Hu, C.S. (2014). Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with SVM. Journal of Applied Research and Technology, 12(6), 1014-1024. Elsevier Ltd. Retrieved April 17, 2021 from .

This record was imported from Journal of Applied Research and Technology on January 29, 2019. Journal of Applied Research and Technology is a publication of Elsevier.

Full text is availabe on Science Direct: http://dx.doi.org/10.1016/S1665-6423(14)71662-1

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