Fingerprint recognition by multi-objective optimization PSO hybrid with SVM

doi:10.1016/S1665-6423(14)71662-1

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/100117

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题名:	Fingerprint recognition by multi-objective optimization PSO hybrid with SVM
作者:	Hsieh, Ching-Tang;Hu, Chia-Shing
贡献者:	淡江大學電機工程學系
关键词:	MOPSO-CD;SVM;fingerprint recognition
日期:	2014-12-01
上传时间:	2015-01-28 11:08:03 (UTC+8)
出版者:	Mexico: Universidad Nacional Autonoma de Mexico * Centro de Ciencias Aplicadas y Desarrollo Tecnologico
摘要:	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.
關聯:	Journal of Applied Research and Technology 12(6), pp.1014-1024
DOI:	10.1016/S1665-6423(14)71662-1
显示于类别:	[電機工程學系暨研究所] 期刊論文

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