Hierarchical Mixtures of Experts and the EM Algorithm

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 coe cients 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.