Genetic algorithms for support vector machine optimization

Fred Gruber, Luis Rabelo, Serge Sala-Diakanda, Luis Robledo, Jose Sepulveda

Resultado de la investigación: Paper

1 Cita (Scopus)

Resumen

Support vector machines are relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This paper deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal parameters for support vector machines. The proposed algorithm is compared with a backpropagation Neural Network in a dataset that represents the NASA Shuttle Columbia Debris database.

Idioma originalEnglish
EstadoPublished - 1 dic 2005
EventoIIE Annual Conference and Exposition 2005 - Atlanta, GA, United States
Duración: 14 may 200518 may 2005

Conference

ConferenceIIE Annual Conference and Exposition 2005
PaísUnited States
CiudadAtlanta, GA
Período14/05/0518/05/05

Huella dactilar

Support vector machines
Genetic algorithms
Classifiers
Neural networks
Backpropagation
Debris
NASA
Learning systems

ASJC Scopus subject areas

  • Engineering(all)

Citar esto

Gruber, F., Rabelo, L., Sala-Diakanda, S., Robledo, L., & Sepulveda, J. (2005). Genetic algorithms for support vector machine optimization. Papel presentado en IIE Annual Conference and Exposition 2005, Atlanta, GA, United States.
Gruber, Fred ; Rabelo, Luis ; Sala-Diakanda, Serge ; Robledo, Luis ; Sepulveda, Jose. / Genetic algorithms for support vector machine optimization. Papel presentado en IIE Annual Conference and Exposition 2005, Atlanta, GA, United States.
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Gruber, F, Rabelo, L, Sala-Diakanda, S, Robledo, L & Sepulveda, J 2005, 'Genetic algorithms for support vector machine optimization', Papel presentado en IIE Annual Conference and Exposition 2005, Atlanta, GA, United States, 14/05/05 - 18/05/05.

Genetic algorithms for support vector machine optimization. / Gruber, Fred; Rabelo, Luis; Sala-Diakanda, Serge; Robledo, Luis; Sepulveda, Jose.

2005. Papel presentado en IIE Annual Conference and Exposition 2005, Atlanta, GA, United States.

Resultado de la investigación: Paper

TY - CONF

T1 - Genetic algorithms for support vector machine optimization

AU - Gruber, Fred

AU - Rabelo, Luis

AU - Sala-Diakanda, Serge

AU - Robledo, Luis

AU - Sepulveda, Jose

PY - 2005/12/1

Y1 - 2005/12/1

N2 - Support vector machines are relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This paper deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal parameters for support vector machines. The proposed algorithm is compared with a backpropagation Neural Network in a dataset that represents the NASA Shuttle Columbia Debris database.

AB - Support vector machines are relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This paper deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal parameters for support vector machines. The proposed algorithm is compared with a backpropagation Neural Network in a dataset that represents the NASA Shuttle Columbia Debris database.

KW - Cross validation

KW - Debris

KW - Genetic algorithms

KW - Support vector machines

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Gruber F, Rabelo L, Sala-Diakanda S, Robledo L, Sepulveda J. Genetic algorithms for support vector machine optimization. 2005. Papel presentado en IIE Annual Conference and Exposition 2005, Atlanta, GA, United States.