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A Universal Approximation Theorem for Mixture of Experts Models
[article]
2016
arXiv
pre-print
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
arXiv:1602.03683v1
fatcat:svv6rphry5fdjfj2rfqlujjjo4