Hierarchical Mixtures of Experts and the EM Algorithm

1 March 1994

journal article
research article
Published by MIT Press in Neural Computation

Vol. 6 (2) , 181-214
https://doi.org/10.1162/neco.1994.6.2.181

Abstract

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.

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