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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

A Radial Basis Function Neural Network Training Mechanism for Pattern Classification Tasks

verfasst von : Antonios D. Niros, George E. Tsekouras

Erschienen in: Unsupervised Learning Algorithms

Verlag: Springer International Publishing

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Abstract

This chapter proposes a radial basis function network learning approach for classification problems that combines hierarchical fuzzy clustering and particle swarm optimization (PSO) with discriminant analysis to elaborate on an effective design of radial basis function neural network classifier. To eliminate the redundant information, the training data are pre-processed to create a partition of the feature space into a number of fuzzy subspaces. The center elements of the subspaces are considered as a new data set which is further clustered by means of a weighted clustering scheme. The obtained cluster centers coincide with the centers of the network’s basis functions. The method of PSO is used to estimate the neuron connecting weights involved in the learning process. The proposed classifier is applied to three machine learning data sets, and its results are compared to other relative approaches that exist in the literature.

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Vorheriges Kapitel Uni- and Multi-Dimensional Clustering Via Bayesian Networks
Nächstes Kapitel A Survey of Constrained Clustering
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Metadaten
Titel
A Radial Basis Function Neural Network Training Mechanism for Pattern Classification Tasks
verfasst von
Antonios D. Niros
George E. Tsekouras
Copyright-Jahr
2016
Buch
Unsupervised Learning Algorithms

Print ISBN: 978-3-319-24209-5

Electronic ISBN: 978-3-319-24211-8

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-24211-8_8

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