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Mixtures of Experts Estimate A Posteriori Probabilities
Type of publication: Conference paper
Citation: Moerland-97.3
Booktitle: Proceedings of the International Conference on Artificial Neural Networks (ICANN'97)
Series: Lecture Notes in Computer Science
Number: 1327
Year: 1997
Publisher: Springer-Verlag
Address: Berlin
Note: (IDIAP-RR 97-07)
Crossref: moerland-97.5:
Abstract: The mixtures of experts (ME) model offers a modular structure suitable for a divide-and-conquer approach to pattern recognition. It has a probabilistic interpretation in terms of a mixture model, which forms the basis for the error function associated with MEs. In this paper, it is shown that for classification problems the minimization of this ME error function leads to ME outputs estimating the a posteriori probabilities of class membership of the input vector.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Moerland, Perry
Editors Gerstner, W.
Germond, A.
Hasler, M.
Nicoud, J. -D.
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Total mark: 0
  • moerland-me-aposteriori.pdf