Abstract: The classical paradigm in mathematical programming is to develop a model that assumes that the input data is precisely known and equal to some nominal values.In practice,the data usually have pertur bations since they are subject to measurement or statistical errors.Therefore,we proposed the Semi-Supervisedv-Support Vector classification algorithm with perturbation in polyhedrons,which are based on formulating the problem as a concave minimization problem.It is solved by a successive linear approxima tion algorithm.Numerical experiments confirm that the parametervis more stabile than parameter C,and the robustness of the proposed method.

Key words: Support Vector Machines, semi-supervised learning, perturbation