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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... (Update)
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BibTeX entry: (Update)
Jordan, M. I., & Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181-214. http://citeseer.ist.psu.edu/jordan94hierarchical.html More
@techreport{ jordan93hierarchical,
author = "Michael I. Jordan and Robert A. Jacobs",
title = "Hierarchical Mixtures of Experts and the {EM} Algorithm",
number = "AIM-1440",
pages = "29",
year = "1993",
url = "citeseer.ist.psu.edu/jordan94hierarchical.html" }
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