Handwritten Digit Recognition by Combining SVM Classifiers
Date Issued
2005-11-21
Author(s)
Gjorgjevikj, Dejan
Chakmakov, Dushan
Abstract
Recent results in pattern recognition have
shown that SVM (Support Vector Machine) classifiers often
have superior recognition rates in comparison to other classification methods. In this paper, a cooperation of four SVM
classifiers for handwritten digit recognition, each using different feature set is examined. We investigate the advantages
and weaknesses of various cooperation schemes based on
classifier decision fusion using statistical reasoning. The obtained results show that it is difficult to exceed the
recognition rate of a single, well-tuned SVM classifier
applied straightforwardly on all feature sets. In our
experiments only one of the cooperation schemes exceeds the
recognition rate of a single SVM classifier. However, the
classifier cooperation reduces the classifier complexity and
need for training samples, decreases classifier training time
and sometimes improves the classifier performance.
shown that SVM (Support Vector Machine) classifiers often
have superior recognition rates in comparison to other classification methods. In this paper, a cooperation of four SVM
classifiers for handwritten digit recognition, each using different feature set is examined. We investigate the advantages
and weaknesses of various cooperation schemes based on
classifier decision fusion using statistical reasoning. The obtained results show that it is difficult to exceed the
recognition rate of a single, well-tuned SVM classifier
applied straightforwardly on all feature sets. In our
experiments only one of the cooperation schemes exceeds the
recognition rate of a single SVM classifier. However, the
classifier cooperation reduces the classifier complexity and
need for training samples, decreases classifier training time
and sometimes improves the classifier performance.
Subjects
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