This paper is concerned with the Quaternion Support Vector Machines for classification as a generalization of the real- and complex-valued Support Vector Machines. In this framework we handle the design of kernels involving the Clifford or quaternion product. The application section shows experimen ts in pattern recognition and colour image processing. We also present a way to expand the amount of classes without the need to increase the number of classifiers as in standar approaches.
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López-González et al. (2016) studied this question.
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