Abstract
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.
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© 1999 Springer-Verlag Wien
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Kokol, M., Grabec, I. (1999). Minimum Square-Error Modeling of the Probability Density Function. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6384-9_26
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DOI: https://doi.org/10.1007/978-3-7091-6384-9_26
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83364-3
Online ISBN: 978-3-7091-6384-9
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