Linear subclass support vector machines

Linear subclass support vector machines

Author: N. Gkalelis, V. Mezaris, I. Kompatsiaris, T. Stathaki

IEEE Signal Processing Letters, vol. 19, no. 9, pp. 575-578, September 2012

In this letter, linear subclass support vector machines (LSSVMs) are proposed that can efficiently learn a piecewise
linear decision function for binary classification problems. This is achieved using a nongaussianity criterion to derive the subclass structure of the data, and a new formulation of the optimization problem that exploits the subclass information. LSSVMs provide low computation cost during training and evaluation, and offer competitive recognition performance in comparison to other popular SVM-based algorithms. Experimental results on various datasets confirm the advantages of LSSVMs.

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