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Global Geometry of SVM Classifiers

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Zhou, D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Zhou, D., Xiao B, Zhou, H., & Dai, R.(2002). Global Geometry of SVM Classifiers.

Abstract

We construct an geometry framework for any norm Support Vector Machine (SVM) classifiers. Within this framework, separating hyperplanes, dual descriptions and solutions of SVM classifiers are constructed by a purely geometric fashion. In contrast with the optimization theory used in SVM classifiers, we have no complicated computations any more. Each step in our theory is guided by elegant geometric intuitions.