Handwritten digit recognition by combining support vector machines using rule-based reasoning
Date Issued
2001-06-22
Author(s)
Gjorgjevikj, Dejan
Chakmakov, Dushan
Radevski, Vladimir
Abstract
The idea of combining classifiers in order to compensate their individual weakness
and to preserve their individual strength has been widely used in recent pattern recognition
applications. In this paper, the cooperation of two feature families for handwritten digit
recognition using SVM (Support Vector Machine) classifiers will be examined. We investigate
the advantages and weaknesses of various decision fusion schemes using rule-based
reasoning. The obtained results show that it is difficult to exceed the recognition rate of the
classifier applied straightforwardly on the feature families as one set. However, the
rule-based cooperation schemes enable an easy and efficient implementation of various
rejection criteria that leads to high reliability recognition systems.
and to preserve their individual strength has been widely used in recent pattern recognition
applications. In this paper, the cooperation of two feature families for handwritten digit
recognition using SVM (Support Vector Machine) classifiers will be examined. We investigate
the advantages and weaknesses of various decision fusion schemes using rule-based
reasoning. The obtained results show that it is difficult to exceed the recognition rate of the
classifier applied straightforwardly on the feature families as one set. However, the
rule-based cooperation schemes enable an easy and efficient implementation of various
rejection criteria that leads to high reliability recognition systems.
Subjects
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