Support Vector Machine

An improved SVM algorithm is proposed based on the theory that support vectors will not appear in the areas which out of the interval between two classes. Benefiting from the concepts of class-radius, class-centroid-distance and class-centripetal force. we can delete those non-SVs effectively with high accuracy and generalization capability even the data was promiscuous. The experimental results show that, comparing with other algorithms; our method achieved a satisfactory result. An improved incremental SVM algorithm is also proposed, which is based on the concepts of Comparability and dependability. The experimental results show that the improved incremental SVM algorithm can make decision-function approach the real one quickly.