A social-spider optimization approach for support vector machines parameters tuning
Carregando...
Fontes externas
Fontes externas
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Institute of Electrical and Electronics Engineers (IEEE)
Tipo
Trabalho apresentado em evento
Direito de acesso
Acesso aberto
Fontes externas
Fontes externas
Resumo
The choice of hyper-parameters in Support Vector Machines (SVM)-based learning is a crucial task, since different values may degrade its performance, as well as can increase the computational burden. In this paper, we introduce a recently developed nature-inspired optimization algorithm to find out suitable values for SVM kernel mapping named Social-Spider Optimization (SSO). We compare the results obtained by SSO against with a Grid-Search, Particle Swarm Optimization and Harmonic Search. Statistical evaluation has showed SSO can outperform the compared techniques for some sort of kernels and datasets.
Descrição
Palavras-chave
Idioma
Inglês
Citação
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - SIS 2014: 2014 IEEE Symposium on Swarm Intelligence, Proceedings, p. 8-13.
