An approach for constructing complex discriminating surfaces based on Bayesian interference of the maximum entropy

In this paper we present a comprehensive Maximum Entropy (MaxEnt) procedure for the classification tasks. This MaxEnt is applied successfully to the problem of estimating the probability distribution function (pdf) of a class with a specific pattern, which is viewed as a probabilistic model handling...

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Bibliographic Details
Published in:Information sciences 2004-06, Vol.163 (4), p.275-291
Main Authors: El Chakik, Fadi, Shahine, Ahmad, Jaam, Jihad, Hasnah, Ahmad
Format: Article
Language:English
Subjects:
Classification
Hebbian learning
Maximum entropy
Neural networks
Probability estimation
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Summary:In this paper we present a comprehensive Maximum Entropy (MaxEnt) procedure for the classification tasks. This MaxEnt is applied successfully to the problem of estimating the probability distribution function (pdf) of a class with a specific pattern, which is viewed as a probabilistic model handling the classification task. We propose an efficient algorithm allowing to construct a non-linear discriminating surfaces using the MaxEnt procedure. The experiments that we carried out shows the performance and the various advantages of our approach.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2003.06.011