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

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.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii

The prominent modeling technique for speech recognition today is the hidden Markov model with Gaussian emission densities. They have suffered, though, from an inability to learn discriminative information and are prone to overfitting and overparameterization. Recent work on machine learning has moved toward models such as the support vector machine that automatically control generalization and ...