Abstract
An error function E for a mixture model is derived from a maximum likelihood approach. The derivation of a gradient descent scheme is performed for both the DSM and the GM networks, and leads to a modified form of the backpropagation algorithm. However, a straightforward application of this method is shown to suffer from considerable inherent convergence problems due to large curvature variations of the error surface. A simple rectification scheme based on a curvature-based shape modification of E is presented.
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© 1999 Springer-Verlag London Limited
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Husmeier, D. (1999). A Maximum Likelihood Training Scheme. In: Neural Networks for Conditional Probability Estimation. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0847-4_3
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DOI: https://doi.org/10.1007/978-1-4471-0847-4_3
Publisher Name: Springer, London
Print ISBN: 978-1-85233-095-8
Online ISBN: 978-1-4471-0847-4
eBook Packages: Springer Book Archive