计算机系统应用
計算機繫統應用
계산궤계통응용
APPLICATIONS OF THE COMPUTER SYSTEMS
2014年
7期
242-245
,共4页
支持向量机%高斯核%参数选择%几何距离%麦克劳林展开
支持嚮量機%高斯覈%參數選擇%幾何距離%麥剋勞林展開
지지향량궤%고사핵%삼수선택%궤하거리%맥극로림전개
support vector machine%Gaussian kernel%parameter selection%geometric distance%McLaughlin expansion
基于高斯核的支持向量机应用很广泛，高斯核参数σ的选择对分类器性能影响很大，本文提出了从核函数性质和几何距离角度来选择参数σ，并且利用高斯函数的麦克劳林展开解决了参数σ的优化选择问题。实验结果表明，该方法能较快地确定核函数参数σ，且 SVM 分类效果较好，解决了高斯核参数σ在实际应用中不易确定的问题。
基于高斯覈的支持嚮量機應用很廣汎，高斯覈參數σ的選擇對分類器性能影響很大，本文提齣瞭從覈函數性質和幾何距離角度來選擇參數σ，併且利用高斯函數的麥剋勞林展開解決瞭參數σ的優化選擇問題。實驗結果錶明，該方法能較快地確定覈函數參數σ，且 SVM 分類效果較好，解決瞭高斯覈參數σ在實際應用中不易確定的問題。
기우고사핵적지지향량궤응용흔엄범，고사핵삼수σ적선택대분류기성능영향흔대，본문제출료종핵함수성질화궤하거리각도래선택삼수σ，병차이용고사함수적맥극로림전개해결료삼수σ적우화선택문제。실험결과표명，해방법능교쾌지학정핵함수삼수σ，차 SVM 분류효과교호，해결료고사핵삼수σ재실제응용중불역학정적문제。
Support vector machine based on Gaussian kernel has been used in many areas. The parameter σ of the Gaussian kernel has great impact on the performance of the classifier. This paper proposes an approach to choose an optimal parameterσbased on the properties of the kernel function and the angle of geometric distance. What is more, we have solved the problem of the optimal option of the parameter σ by means of the McLaughlin expansion of the Gaussian kernel function. The experiment results indicate that this method can get parameter σ very quickly and can achieve high efficiency. Thus the difficulty of the estimation of the parameterσcan be solved by our method.
