Hierarchical mixtures of experts and the EM algorithm
Jordan, M.I.; Jacobs, R.A.
Neural Networks, 1993. IJCNN apos;93-Nagoya. Proceedings of 1993 International Joint Conference on
Volume 2, Issue , 25-29 Oct. 1993 Page(s): 1339 - 1344 vol.2
Digital Object Identifier   10.1109/IJCNN.1993.716791
Summary: 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 (GLIMs). 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 online learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.

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