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Hierarchical Mixtures of Experts and the EM Algorithm

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ICANN ’94 (ICANN 1994)

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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.

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References

  • Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Belmont, CA: Wadsworth International Group.

    MATH  Google Scholar 

  • 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.

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  • Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19, 1–141.

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  • 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.

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  • Jordan, M. I. & Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181–214.

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  • McCullagh, P. & NeIder, J.A. (1983). Generalized Linear Models. London: Chapman and Hall.

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© 1994 Springer-Verlag London Limited

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Jordan, M.I., Jacobs, R.A. (1994). Hierarchical Mixtures of Experts and the EM Algorithm. In: Marinaro, M., Morasso, P.G. (eds) ICANN ’94. ICANN 1994. Springer, London. https://doi.org/10.1007/978-1-4471-2097-1_113

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  • DOI: https://doi.org/10.1007/978-1-4471-2097-1_113

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  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19887-1

  • Online ISBN: 978-1-4471-2097-1

  • eBook Packages: Springer Book Archive

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