Empirical Assessment of Classification Accuracy of Local SVM

Segata, Nicola and Blanzieri, Enrico (2008) Empirical Assessment of Classification Accuracy of Local SVM. UNSPECIFIED. (Submitted)

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    The combination of maximal margin classifiers and k-nearest neighbors rule constructing an SVM on the neighborhood of the test sample in the feature space (called kNNSVM), was presented as a promising way of improving classification accuracy. Since no extensive validation of the method was performed yet, in this work we test the kNNSVM method on 13 widely used datasets using four different kernels obtaining good classification results. Moreover we present two artificial datasets in which kNNSVM performs substantially better than SVM with RBF kernel. Statistically significant testing of the method as well as the results on the artificial datasets, lead us to conclude that kNNSVM performs sensibly better than SVM.

    Item Type: Departmental Technical Report
    Department or Research center: Information Engineering and Computer Science
    Subjects: Q Science > QA Mathematics > QA076 Computer software
    Report Number: DISI-08-014
    Repository staff approval on: 26 Mar 2008

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