Abstract
Support Vector Machine (SVM) is a new machine learning method. K-Nearest-Neighbor (KNN) is a non-parameter classifying method, which is quite effective and easy to use. KNN has been widely used in classification, regression and pattern recognition. A new algorithm that combining SVM with KNN is presented, which is called a new kernel learning method (Modified Support Vector Machine, MSVM) to be used for classification. Inspired by the intuitive geometric interpretation of SVM based on convex hulls, it maps the data in the original space to the kernel space with the kernel trick and constructs a nearest neighbor classifier in the kernel space, which takes the convex hulls of training sets as the extended classifies sets. Then, KNN will be used. Itpsilas proved that the modified SVM algorithm is feasible and less sensitive to the parameter K along with better accuracy.
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