Abstract
The problem of nonparametric probability density estimation using neural networks methodologies is addressed here. We investigate a criterion that leads to an appropriate choice of the network architecture complexity. In the present work each unknown density is approximated in terms of a linear combination of radial basis functions (RBFs). Both the parameters of the approximating function and the number of RBFs units are estimated using a modified Kullback-Leibler distance as a criterion of optimality. This modification consists of the addition of a term that penalizes complex architectures. Experimental results show the reliability of the methodology.
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