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

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

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

Conference proceedings International

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

Pages: 1472-1477

22nd International Conference on Pattern Recognition (ICPR)

Stockholm, SWEDEN, AUG 24-28, 2014

Indexing

ISI Proceedings

ISI Web of Knowledge - 4 Citations

ISI Web of Science

Scopus - 11 Citations

INSPEC

Scientific classification

FOS: Engineering and technology

CORDIS: Technological sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6