CAJ | 학술논문

提出一种新的三阶分段光滑函数，构造三阶光滑支持向量机模型( TPSSVM)。理论证明新三阶分段光滑函数对正号函数的逼近程度更高。在处理多类问题时，提出一种基于编码方式的一对多光滑支持向量机分类方法。对于人脸识别问题，通过主成分分析( PCA)进行特征提取，并利用多分类光滑支持向量机对人脸特征图像进行训练和测试。应用于ORL人脸库和FERET人脸库的测试结果表明，多分类光滑支持向量机比传统的识别方法有更高的识别率。
제출일충신적삼계분단광활함수，구조삼계광활지지향량궤모형( TPSSVM)。이론증명신삼계분단광활함수대정호함수적핍근정도경고。재처리다류문제시，제출일충기우편마방식적일대다광활지지향량궤분류방법。대우인검식별문제，통과주성분분석( PCA)진행특정제취，병이용다분류광활지지향량궤대인검특정도상진행훈련화측시。응용우ORL인검고화FERET인검고적측시결과표명，다분류광활지지향량궤비전통적식별방법유경고적식별솔。
A new three-order piecewise function was used to smoothen the model of Support Vector Machine ( SVM) and a Third-order Piecewise Smooth SVM ( TPSSVM) was proposed. By theory analyzing, approximation accuracy of the smooth function to the plus function is higher than that of the available. When dealing with the multi-class problem, a coding method of multi-class classification based on one-against-rest was proposed. Principal Component Analysis ( PCA) was employed to extract the main features of face image set, and multi-class classification of smooth SVM was used for face recognition. The experimental results on ORL and FERET face databases show that the recognition rate of smooth SVM for multi-class classification is better than the traditional identification methods.