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Many supervised machine learning tasks can be cast as multi-class classification problems. Support vector machines (SVMs) excel at binary classification problems, but the elegant theory behind large-margin hyperplane cannot be easily extended to their multi-class counterparts. On the other hand, it was shown that the decision hyperplanes for binary classification obtained by SVMs are equivalent to the solutions obtained by Fisher's linear discriminant on the set of support vectors. Discriminantdoi:10.1007/s10115-006-0013-y fatcat:iiz7b4aqufgu7h3rwj3u6d6xau