Abstract
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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