We propose a classifier which finds a finite number of convex hulls to approximate class regions and carries out discrimination on the basis of nearness of a sample to those convex hulls. Every convex hull holds the following properties: it should include samples form one class only and should be as large as possible. Compared with support vector machines, through some experiments with low-dimensional datasets, we confirmed that both methods are comparable in performance.
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