Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering

In training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or some supervision can be introduced, for example, by concatenating the input vect...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 27; H. 12; S. 2458 - 2471
Hauptverfasser:	Raitoharju, Jenni, Kiranyaz, Serkan, Gabbouj, Moncef
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Centroids Classification Clustering Clustering algorithms Clustering methods Computational neuroscience Heuristic algorithms Neural networks Neurons Optimization Particle swarm optimization particle swarm optimization (PSO) Radial basis function radial basis function networks (RBFNNs) supervised learning Training
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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