Ching-Tang Hsieh
Department of Electrical Engineering, Tamkang University Taipei County, Taiwan, Republic of China
Chia-Shing Hu
Department of Electrical Engineering, Tamkang University Taipei County, Taiwan, Republic of China
ABSTRACT
The problem of fingerprint classification is discussed for many years. Support Vector Machine (SVM) is a traditional artificial intelligence algorithm developed for dealing classification problems. In this study, have 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 andparameters for this algorithm can only be determined by users experience, such as penalty parameter. Therefore, this algorithm is developed to help user prevent from suffering to use this algorithm in the above condition. It is has successfully proved that user do not need to make experiment to determine the penalty parameter C. NIST-4 database is used to assess the proposed algorithm. The experiment results show the method can get good classification results.
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How to cite this article
Ching-Tang Hsieh and Chia-Shing Hu, 2013. Using Multi-objective Optimization PSO in SVM for Fingerprint Recognition. Journal of Applied Sciences, 13: 3705-3711.
DOI: 10.3923/jas.2013.3705.3711
URL: https://scialert.net/abstract/?doi=jas.2013.3705.3711
DOI: 10.3923/jas.2013.3705.3711
URL: https://scialert.net/abstract/?doi=jas.2013.3705.3711
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