Hierarchical Mixtures of Experts and the EM Algorithm

  title={Hierarchical Mixtures of Experts and the EM Algorithm},
  author={Michael I. Jordan and Robert A. Jacobs},
  journal={Neural Computation},
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… CONTINUE READING
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