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A Universal Approximation Theorem for Mixture of Experts Models by Nguyen, Hien D; Lloyd-Jones, Luke R; McLachlan, Geoffrey J is a scholarly article available to read on EtoBox.

What is A Universal Approximation Theorem for Mixture of Experts Models about?

The mixture of experts (MoE) model is a popular neural network architecture for nonlinear regression and classification. The class of MoE mean functions is known to be uniformly convergent to any unknown target function, assuming that the target function is from Sobolev space that is sufficiently differentiable and that the domain of estimation is a compact unit hypercube. We provide an alternative result, which shows that the class of MoE mean functions is dense in the class of all continuous functions over arbitrary compact domains of estimation. Our result can be viewed as a universal approximation theorem for MoE models.

Author

Nguyen, Hien D; Lloyd-Jones, Luke R; McLachlan, Geoffrey J

Published

2016

Language

EN