Minimum Square-Error Modeling of the Probability Density Function
Training of normalized radial basis function neural networks can be considered as a probability density function estimation of the experimental data. A new unsuper-vised method of probability density function estimation is proposed. The method is applied to a multivariate Gaussian mixture model. Batch-mode learning equations are derived and some simple examples are given. Training method is called a minimum square-error modeling of the probability density function. It is similar to the maximum-likelihood method but is numerically less demanding.
KeywordsMixture Model Elapse Time Gaussian Mixture Model Radial Basis Function Neural Network Probability Density Function Estimation
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