The support-vector network is a new learning model for two-group classification problems. The model conceptually implements the following idea: input vectors are non-linearly functions mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning model. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors.