Differentiating features for the F distributions with different degrees of freedom through RBF network pruning with QLP
نویسنده
چکیده
This paper proposes an artificial neural network RBF to classification using feature descriptors. The theoretical and practical aspects of theory F distributions with different degrees of freedom introduced. The distribution F densities are similar in shape, making it difficult to identify the differences between the two densities. This paper is concerned with separating these same probability densities with different degrees of freedom using feature descriptors, identified by pruning a Radial Basis Function (RBF) network using pivoted QLP decomposition generated for densities function, and its validity were evaluated by the rate of correct classification. The QLP method proves efficient for reducing the network size by pruning hidden nodes, resulting is a parsimonious model which identifies four main features (namely kurtosis and skewness and mean). The classification model induced by the methodology show, in general, good results. Key-Words: Radial Basis Function, Pivoted QLP Decomposition, Pruning, F distribution
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