KMOD - A Tw o-Parameter SVM Kernel for Pattern Recognition
نویسندگان
چکیده
It has been shown that Support Vector Machine theory optimizes a smoothness functional hypothesis through kernel application. We present KMOD, a two-parameter SVM kernel with distinctive properties of good discrimination between patterns while preserving the data neighborhood information. In classi£cation problems, the experiments we carried out on the Breast Cancer benchmark produced better performance than RBF kernel and some state of the art classi£ers. As well, it also generated favorable results when subjected to a 10-class problem of recognizing handwritten digits in the NIST database.
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