Modification of SVM’s optimal hyperplane based on minimal mistake

Since some value of error penalties C in C-support vector machine (C-SVM) may cause extreme and irrational optimal separating hyperplanes, a new modification of SVM’s optimal hyperplane was proposed. By modifying the distance restriction of separating hyperplane between positive and negative classes, the bias coefficient was calculated with minimal training samples’ total error, while the absolute value of the error difference between positive and negative classes was balanced considered, a better separating hyperplane with minimal mistake was obtained. The experimental results show that this algorithm has improved the classified precision and enhanced the ability of reducing the outliers and noises’ effect, compared to C-SVM and other modification algorithm.