On the training of artificial neural networks with radial basis function using optimum-path forest clustering

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

Gustavo H. Rosa (Autor) Outra; Kelton A. P. Costa (Autor) Outra; Leandro A. Passos Júnior (Autor) Outra; João P. Papa (Autor) Outra; Alexandre X. Falcão (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP

Ata de Conferência Internacional

Título: 2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)

Páginas: 1472-1477

22nd International Conference on Pattern Recognition (ICPR)

Stockholm, SWEDEN, AUG 24-28, 2014

Indexação

ISI Proceedings

ISI Web of Knowledge - 4 Citações

ISI Web of Science

Scopus - 11 Citações

INSPEC

Classificação Científica

FOS: Ciências da engenharia e tecnologias

CORDIS: Ciências Tecnológicas

Outras Informações

ID Authenticus: P-00G-12X

DOI: 10.1109/ICPR.2014.262

Abstract (EN): In this paper, we show how to improve the Radial Basis Function Neural Networks effectiveness by using the Optimum-Path Forest clustering algorithm, since it computes the number of clusters on-the-fly, which can be very interesting for finding the Gaussians that cover the feature space. Some commonly used approaches for this task, such as the well known k-means, require the number of classes/clusters previous its performance. Although the number of classes is known in supervised applications, the real number of clusters is extremely hard to figure out, since one class may be represented by more than one cluster. Experiments over 9 datasets together with statistical analysis have shown the suitability of OPF clustering for the RBF training step.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6