Abstract
Support Vector Machine (SVM) is a new learning method based on statistical learning theory and structural optimization. Because of the excellent performance, SVM is selected for big data analysis applications. Present research deals with the parameter optimization in SVM using a dual SVM model. Higher classification accuracy will be acquired with suitable parameters selection compared to other algorithms. The proposed Dual SVM kernel has some optimal parameters which may be calculated by the best fitness values of existing parameters. The effectiveness of Dual SVM is validated through different data sets from UCI machine learning data base. The training procedure has improved the speed of computation and classification accuracy.
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