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Evolutionary q-Gaussian radial basis function neural networks for multiclassification
- Source :
- Neural Networks. 24:779-784
- Publication Year :
- 2011
- Publisher :
- Elsevier BV, 2011.
-
Abstract
- This paper proposes a radial basis function neural network (RBFNN), called the q -Gaussian RBFNN, that reproduces different radial basis functions (RBFs) by means of a real parameter q . The architecture, weights and node topology are learnt through a hybrid algorithm (HA). In order to test the overall performance, an experimental study with sixteen data sets taken from the UCI repository is presented. The q -Gaussian RBFNN was compared to RBFNNs with Gaussian, Cauchy and inverse multiquadratic RBFs in the hidden layer and to other probabilistic classifiers, including different RBFNN design methods, support vector machines (SVMs), a sparse classifier (sparse multinomial logistic regression, SMLR) and a non-sparse classifier (regularized multinomial logistic regression, RMLR). The results show that the q -Gaussian model can be considered very competitive with the other classification methods.
- Subjects :
- Radial basis function network
business.industry
Cognitive Neuroscience
Gaussian
Computer Science::Neural and Evolutionary Computation
Normal Distribution
Cauchy distribution
Inverse
Pattern recognition
Support vector machine
symbols.namesake
Artificial Intelligence
Classifier (linguistics)
symbols
Radial basis function
Neural Networks, Computer
Artificial intelligence
business
Gaussian network model
Algorithms
Mathematics
Subjects
Details
- ISSN :
- 08936080
- Volume :
- 24
- Database :
- OpenAIRE
- Journal :
- Neural Networks
- Accession number :
- edsair.doi.dedup.....536436eb7e0056fe53f585ec0ca8c705