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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Belmont, CA: Wadsworth International Group.
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.
Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19, 1–141.
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.
Jordan, M. I. & Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181–214.
McCullagh, P. & NeIder, J.A. (1983). Generalized Linear Models. London: Chapman and Hall.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1994 Springer-Verlag London Limited
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-1-4471-2097-1_113
Published:
Publisher Name: Springer, London
Print ISBN: 978-3-540-19887-1
Online ISBN: 978-1-4471-2097-1
eBook Packages: Springer Book Archive