Online learning algorithm of kernel-based ternary classifiers using support vectors

Authors:
A. V. Kovalchuk;N. S. Bellyustin
Affiliations:
Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, Russia;Radiophysical Scientific-Research Institute, Nizhny Novgorod, Russia
Venue:
Optical Memory and Neural Networks
Year:
2013

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Abstract

An algorithm OnSVM of the kernel-based classification is proposed which solution is very close to -SVM an efficient modification of support vectors machine. The algorithm is faster than batch implementations of -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.