【摘要】 将最小二乘支持向量机(leastsquare support vector machine,LS-SVM)算法用于杭州南宋官窑2窑址出土瓷片的分类研究中,根据瓷片胎和釉的主要、次要和痕量元素组成对它们进行了分类,用留一法检验其分类效果,并与支持向量机(support vector machine,SVM)算法和自组织特征映射(self-organizing map,SOM)算法进行了比较。结果表明:SVM算法和LS-SVM算法比SOM算法更适合于处理"小样本"问题;一般情况下,SVM的分类效果比LS-SVM的分类效果好,但是LS-SVM具有更快的求解速度。更多还原

【Abstract】 Ancient ceramic pieces for two types of official ware in the Southern Song Dynasty were classified by the least squares support vector machine(LS–SVM) algorithm according to the discrepancies in the major,minor and trace elements in the bodies and glazes.The classification effect was validated by the leave-one-out method and compared with the support vector machine(SVM) and self-organizing map(SOM) methods.The results show that the methods of SVM and LS–SVM are preferable to SOM for classifying small samples.Generally,SVM provides a more accurate classification than does LS–SVM;however,the calculation of LS–SVM is quicker than that of SVM when run in MATLAB.更多还原