COOPERATION OF SUPPORT VECTOR MACHINES FOR HANDWRITTEN DIGIT RECOGNITION TROUGH PARTITIONING OF THE FEATURE SET

In this paper, various cooperation schemes of SVM (Support Vector Machine) classifiers applied on two feature sets for handwritten digit recognition are examined. We start with a feature set composed of structural and statistical features and corres- ponding SVM classifier applied on the comp- lete feature set. Later, we investigate the vari- ous partitions of the feature set as well as the advantages and weaknesses of various decisi- on fusion schemes applied on SVM classifiers designed for partitioned feature sets. The ob- tained results show that it is difficult to exce- ed the recognition rate of a single SVM classi- fier applied straightforwardly on the comple- te feature set. Additionally, we show that the partitioning of the feature set according to feature nature (structural and statistical fea- tures) is not always the best way for designing classifier cooperation schemes. These results impose need of special feature selection pro- cedures for optimal partitioning of the featu- re set for classifier cooperation schemes.

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