Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/76475
Author(s): Gustavo H. Rosa
Kelton A. P. Costa
Leandro A. Passos Júnior
João P. Papa
Alexandre X. Falcão
João Manuel R. S. Tavares
Title: On the training of artificial neural networks with radial basis function using optimum-path forest clustering
Issue Date: 2014
Abstract: 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.
Subject: Ciências Tecnológicas, Ciências da engenharia e tecnologias
Technological sciences, Engineering and technology
Scientific areas: Ciências da engenharia e tecnologias
Engineering and technology
URI: https://repositorio-aberto.up.pt/handle/10216/76475
Source: 2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: openAccess
License: https://creativecommons.org/licenses/by-nc/4.0/
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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