Online Learning Algorithm of Kernel-Based Ternary Classifiers Using Support Vectors

摘要

An algorithm OnSVM of the kernel-based classification is proposed which solution is very close to v-SVM an efficient modification of support vectors machine. The algorithm is faster than batch implementations of v -SVM and has a smaller resulting number of support vectors. The approach developed maximizes a margin between a pair of hyperplanes in feature space and can be used in online setup. A ternary classifier of 2-class problem with an "unknown " decision is constructed using these hyperplanes.