KMOD - a two-parameter SVM kernel for pattern recognition

It has been shown that the support vector machine (SVM) theory optimizes a smoothness functional hypothesis through kernel applications. We present KMOD, a two-parameter SVM kernel with distinctive properties of good discrimination between patterns while preserving the data neighborhood information. In classification problems, the experiments we carried out on the breast cancer benchmark produced better performance than the RBF kernel and some state of the art classifiers. It also generated favorable results when subjected to a 10-class problem of recognizing handwritten digits in the NIST database.