International Journal of Innovations in Engineering and Technology (IJIET)
In this paper, the authors are comparing the algorithms of Support Vector Machine (SVM) and Kernel Principal Component Analysis (KPCA) to improve the accuracy and performance of the numerical dataset. SVM fails to consider global information and has high computational complexity. Along with LDA, SVM overcomes the above disadvantages but cannot be applied to non-linear data. Along with KDA, SVM overcomes all the above disadvantages and gives good accuracy. KPCA is used instead of SVM to get better accuracy than SVM.