Online Support Vector Machine Based on Minimum Euclidean Distance

The present study includes development of an online support vector machine (SVM) based on minimum euclidean distance (MED). We have proposed a MED support vector algorithm where SVM model is initialized with small amount of training data and test data is merged to SVM model for incorrect predictions only. This method provides a simpler and more computationally efficient implementation as it assign previously computed support vector coefficients. To merge test data in SVM model, we find the euclidean distance between test data and support vector of target class and the coefficients of MED of support vector of training class are assigned to test data. The proposed technique has been implemented on benchmark data set mnist where SVM model initialized with 20K images and tested for 40K data images. The proposed technique of online SVM results in overall error rate as 1.69% and without using online SVM results in error rate as 7.70%. The overall performance of the developed system is stable in nature and produce smaller error rate.