Support Vector Machine Improvement with Kemel-Based Nonlinear Representor

By introducing the idea of optimal feature representation in kernel-based nonlinear representor (KNR), the traditional support vector machine (SVM) is improved in this manuscript to a new version called KNR-SVM. Firstly, the k-means unsupervised method is adopted to cluster the input patterns to form k-centers. Then, a mapping function, a point of a reproducing kernel Hilbert space (RKHS), is estimated based on the k-centers and KNR method, and the input patterns are mapped explicitly into a higher dimensional feature space, using the estimated function. Finally, the traditional SVM is taken as a classifier to classify the mapped patterns. Experimental results on handwritten digital recognition show that the proposed KNRSVM outperforms the traditional SVM.