Abstract
The structure of a universal approximator network for predicting conditional probability densities is derived, and it is shown that the resulting architecture can deal with both stochastic and determinstic processes. Two variants, the derivative-of-sigmoid mixture (DSM) and the Gaussian mixture (GM) networks are presented, and their relation to a stochastic kernel expansion is noted. The chapter concludes with a comparison between these models and several relevant alternative approaches which have recently been introduced to the neural network community.
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© 1999 Springer-Verlag London Limited
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Husmeier, D. (1999). A Universal Approximator Network for Predicting Conditional Probability Densities. In: Neural Networks for Conditional Probability Estimation. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0847-4_2
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DOI: https://doi.org/10.1007/978-1-4471-0847-4_2
Publisher Name: Springer, London
Print ISBN: 978-1-85233-095-8
Online ISBN: 978-1-4471-0847-4
eBook Packages: Springer Book Archive