Unsupervised pattern classification by neural networks

D. Hamad, C. Firmin and J.-G. Postaire

Mathematics and Computers in Simulation (MATCOM), 1996, vol. 41, issue 1, 109-116

Abstract: A neural network is applied to the unsupervised pattern classification approach. Given a set of data consisting of unlabeled samples from several classes, the task of unsupervised classification is to label every sample in the same class by the same symbol such that the data set is divided into several clusters. We consider the hypothesis that the data set is drawn from a finite mixture of Gaussian distributions. The network architecture is a two-layer feedforward type: the units of the first layer are Gaussians and each correspond to one component of the mixture. The output layer provides the probability density estimation of the mixture. The weighted competitive learning is used to estimate the mean vectors and the non-diagonal covariance matrices of the Gaussian units. The number of Gaussian units in the hidden layer is optimized by informational criteria. Some of the results are reported, and the performance of this approach is evaluated.

Date: 1996
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0378475495000631
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:41:y:1996:i:1:p:109-116

DOI: 10.1016/0378-4754(95)00063-1

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().