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 ...

X. CAO{{, J. CHEN*{, B. MATSUSHITA§, H. IMURA" and L. WANG{{ {Key Laboratory of Environment Change and Natural Disaster, Ministry of Education of China, College of Resource Science and Technology, Beijing Normal University, Beijing, 100875, China {Graduate School of Engineering, Nagoya University, Nagoya 464-8603, Japan §Graduate School of Life and Environmental Sciences, University of Tsukuba,...

EYE DETECTION USING DISCRIMINATORY FEATURES AND AN EFFICIENT SUPPORT VECTOR MACHINE

The support vector machine methodology is a rapidly growing area of research in machine learning. A number of computational learning theoretical type results on the support vector machine have appeared in the machine learning literature. Typically the generalization error of the support vector machine is shown to be bounded by quantities that are related to the empirical margin of the training ...

We describe in a rudimentary fashion how SVM (support vector machine) plays the role of classifier in a mathematical setting. We then discuss its application in the study of multiple SNP(single nucleotide polymorphism) variations. Also presented is a set of preliminary test results with clinical data.