ICANN ’94 pp 479-486 | Cite as

Hierarchical Mixtures of Experts and the EM Algorithm

  • M. I. Jordan
  • R. A. Jacobs
Conference paper

Abstract

In the statistical literature and in the machine learning literature, divide-and-conquer algorithms have become increasingly popular. The CART algorithm (Breiman, et al., 1984) and the MARS algorithm (Friedman, 1991) are well-known examples. These algorithms fit surfaces to data by explicitly dividing the input space into a nested sequence of regions, and by fitting simple surfaces (e.g., constant functions) within these regions. The advantages of these algorithms include the interpretability of their solutions and the speed of the training process.

Keywords

lIME Cond 

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References

  1. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Belmont, CA: Wadsworth International Group.MATHGoogle Scholar
  2. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. B, 39, 1–38.MATHMathSciNetGoogle Scholar
  3. Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19, 1–141.CrossRefMATHMathSciNetGoogle Scholar
  4. Jordan, M. I. & Jacobs, R. A. (1992). Hierarchies of adaptive experts. In J. Moody, S. Hanson, & R. Lippmann (Eds.), Advances in Neural Information Processing Systems 4. San Mateo, CA: Morgan Kaufmann.Google Scholar
  5. Jordan, M. I. & Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181–214.CrossRefGoogle Scholar
  6. McCullagh, P. & NeIder, J.A. (1983). Generalized Linear Models. London: Chapman and Hall.MATHGoogle Scholar

Copyright information

© Springer-Verlag London Limited 1994

Authors and Affiliations

  • M. I. Jordan
    • 1
  • R. A. Jacobs
    • 2
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA
  2. 2.University of RochesterRochesterUSA

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