(Enter summary)
Abstract: This paper presents a number of proofs that
equate the outputs of a Multi-Layer Perceptron
(MLP) classifier and the optimal Bayesian discriminant
function for asymptotically large sets of
statistically independent training samples. Two
broad classes of objective functions are shown to
yield Bayesian discriminant performance. The
first class are "reasonable error measures," which
achieve Bayesian discriminant performance by
engendering classifier outputs that asymptotically
equate to a ... (Update)
Context of citations to this paper: More
...of error is minimized. Under certain circumstances, neural networks trained with BackProp will attempt to approximate this approach [10]. Now, since consistently assigns the most probable class to y, the probability distribution of Omega for a particular value of y, jY =...
...The first is modular design methods (for instance, of. 6, 7, 20, 21, 22] the second is Bayesian statistics (for instance, cf.[2, 1, 11, 12, 27]) and in pm ticular the Maximum A Poster ior i (MAP) classification rule (see [25] To this end we use the Pm tition Algorithm...
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BibTeX entry: (Update)
J. B. II Hampshire and B. A. Perlmutter. Equivalence proofs for multilayer perceptron classifiers and the Bayesian discriminant function. Proceedings of the 1990 Connectionist Models Summer School, 1990. D. Touretzky, J. Elman, T. Sejnowski, and G. Hinton, eds. Morgan Kaufmann, San Mateo, CA. http://citeseer.ist.psu.edu/hampshire90equivalence.html More
@inproceedings{ hampshire90equivalence,
author = "J. B. {II} Hampshire and B. A. Perlmutter",
title = "Equivalence proofs for multilayer perceptron classifiers and the {B}ayesian discriminant function",
booktitle = ""Proceedings of the 1990 Connectionist Models Summer School, 1990. D. Touretzky, J. Elman, T. Sejnowski, and G. Hinton, eds. Morgan Kaufmann, San Mateo, {CA}."",
year = "1990",
url = "citeseer.ist.psu.edu/hampshire90equivalence.html" }
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