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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/100117


    Title: Fingerprint recognition by multi-objective optimization PSO hybrid with SVM
    Authors: Hsieh, Ching-Tang;Hu, Chia-Shing
    Contributors: 淡江大學電機工程學系
    Keywords: MOPSO-CD;SVM;fingerprint recognition
    Date: 2014-12-01
    Issue Date: 2015-01-28 11:08:03 (UTC+8)
    Publisher: Mexico: Universidad Nacional Autonoma de Mexico * Centro de Ciencias Aplicadas y Desarrollo Tecnologico
    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 multiobjective 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.
    Relation: Journal of Applied Research and Technology 12(6), pp.1014-1024
    DOI: 10.1016/S1665-6423(14)71662-1
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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