MATOS, L. N.; MATOS, Leonardo Nogueira.
Resumo:
The classification problem in pattern recognition can be viewed as a probability distribution
estimation task. Recent developments try t o model it as a weight sum of distributions which
is a parametric approach, since weights and parameters should be estimated. In this work the
target distribution is reached without the need to estimate parameters from a model distribution.
Considering that the output of classifiers are probability measurements, a Bayesian network is
used t o combine local and global classifiers. Briefly, the main objective of this work is to present
a methodology that establishes how t o partition the feature space in order to generate a set of
classifiers and group them in a framework that combines their outputs.
A case study was developed for a handwritten digit recognition application. The results reveal
that the proposed system is competitive with the best classifiers pointed in the literature.
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