Generalization error bounds in Support Vector Machines are based on the minimum distance between training points and the separating hyperplane. The error of soft margin algorithm can be bounded by a target margin and some norms of the slack vector. In this paper, we propose a new method controlling allowable error and formulate considering the contamination by noise in data directly. The method can provide desirable separating hyperplanes easily by controlling a restricted slack parameter. Additionally, through an artificial numerical example, we compare the proposed method with a conventional soft margin algorithm.
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