ML Anthology

Hierarchical Mixtures of Experts and the EM Algorithm

Michael I. Jordan, Robert A. Jacobs
NeCo 1994 pp. 181-214
doi:10.1162/NECO.1994.6.2.181 /neco/1994/jordan1994neco-hierarchical/

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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Cite

Text

Jordan and Jacobs. "Hierarchical Mixtures of Experts and the EM Algorithm." Neural Computation, 1994. doi:10.1162/NECO.1994.6.2.181

Markdown

[Jordan and Jacobs. "Hierarchical Mixtures of Experts and the EM Algorithm." Neural Computation, 1994.](https://mlanthology.org/neco/1994/jordan1994neco-hierarchical/) doi:10.1162/NECO.1994.6.2.181

BibTeX

@article{jordan1994neco-hierarchical,
  title     = {{Hierarchical Mixtures of Experts and the EM Algorithm}},
  author    = {Jordan, Michael I. and Jacobs, Robert A.},
  journal   = {Neural Computation},
  year      = {1994},
  pages     = {181-214},
  doi       = {10.1162/NECO.1994.6.2.181},
  volume    = {6},
  url       = {https://mlanthology.org/neco/1994/jordan1994neco-hierarchical/}
}