A Novel Method to enhance the Recognition performance of an SVM

摘要

A novel method to enhance the recongnition performance of an SVM (Support Vectot Machine) is proposed.The class label of each feature vector in the dataset is added in the corresponding feature vector as a feature value,which build a new vector called adding-after feature vector,all of which compose the adding-after dataset.It is demostrated that an SVM based on the adding-after dataset has advantages such as high generilization performance and little structure risk,compared with an SVM based on the original dataset.When predicting the unkown feature vector,different class labels (1 and -1) are respectively added to the unkown feature vector,and 2 adding-after feature vectors are got.Two hyperplane function values are obtained by substituting the 2 adding-after feature vectors into the hyperplane function respectively,and the symol (1 or -1) of the function value with larger absolute value is conducted as the class label of the unkown feature vector.Experiments results show that the proposed mehtod can enhance the recognition performance of an SVM effectively.