Improving the Performance of the Support Vector Machine: Two Geometrical Scaling Methods
نویسندگان
چکیده
In this chapter, we discuss two possible ways of improving the performance of the SVM, using geometric methods. The first adapts the kernel by magnifying the Riemannian metric in the neighborhood of the boundary, thereby increasing separation between the classes. The second method is concerned with optimal location of the separating boundary, given that the distributions of data on either side may have different scales.
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