Genetic algorithms for support vector machine optimization

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

Resultado de la investigación: Contribución a los distintos tipos de conferenciaArtículo

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 originalInglés
EstadoPublicada - 1 dic 2005
EventoIIE Annual Conference and Exposition 2005 - Atlanta, GA, Estados Unidos
Duración: 14 may 200518 may 2005

Conferencia

ConferenciaIIE Annual Conference and Exposition 2005
PaísEstados Unidos
CiudadAtlanta, GA
Período14/05/0518/05/05

Áreas temáticas de ASJC Scopus

  • Ingeniería (todo)

Huella Profundice en los temas de investigación de 'Genetic algorithms for support vector machine optimization'. En conjunto forman una huella única.

  • 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, Estados Unidos.