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336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

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351.#.#.b: Journal of Applied Research and Technology

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850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/147/144

100.1.#.a: Hsieh, Ching Tang; Hu, Chia Shing

524.#.#.a: Hsieh, Ching Tang, et al. (2014). Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with SVM. Journal of Applied Research and Technology; Vol. 12 Núm. 6. Recuperado de https://repositorio.unam.mx/contenidos/45762

245.1.0.a: Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with SVM

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2014

264.#.1.c: 2014-12-01

653.#.#.a: MOPSO-CD; SVM; fingerprint recognition

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041.#.7.h: eng

520.3.#.a: 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.

773.1.#.t: Journal of Applied Research and Technology; Vol. 12 Núm. 6

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

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

harvesting_date: 2023-11-08 13:10:00.0

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Artículo

Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with SVM

Hsieh, Ching Tang; Hu, Chia Shing

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Hsieh, Ching Tang, et al. (2014). Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with SVM. Journal of Applied Research and Technology; Vol. 12 Núm. 6. Recuperado de https://repositorio.unam.mx/contenidos/45762

Descripción del recurso

Autor(es)
Hsieh, Ching Tang; Hu, Chia Shing
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with SVM
Fecha
2014-12-01
Resumen
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.
Tema
MOPSO-CD; SVM; fingerprint recognition
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces