CAJ | 학술논문

利用三种不同的聚类方法——利用类均值的最小距离聚类、利用类均值的最大距离聚类和利用最大间隔准则聚类，提出了三个基于SVM的决策树多类分类器．为了检验所提算法的有效性和先进性，对AVIRIS遥感图像进行了实验．实验结果表明，本文所提的三种算法明显好于最小距离分类法、线性判别分类法、决策树分类法、OAR—SVM和0AO-SVM．
이용삼충불동적취류방법——이용류균치적최소거리취류、이용류균치적최대거리취류화이용최대간격준칙취류，제출료삼개기우SVM적결책수다류분류기．위료검험소제산법적유효성화선진성，대AVIRIS요감도상진행료실험．실험결과표명，본문소제적삼충산법명현호우최소거리분류법、선성판별분류법、결책수분류법、OAR—SVM화0AO-SVM．
In this paper, three kinds of decision tree multi-class classifiers based on SVM are pres- ented by means of three clustering methods, which are respectively clustering with minimum distance of class means, maximum distance of class means and maximum margin criteria. The experiments with AVIRIS remote sensing image are made for testing the validity and advantage of our proposed algo- rithms. The experimental results demonstrate that our methods are significantly better than minimum distance classification, linear discriminant classification, decision tree classification, OAR-SVM and OAO-SVM.