应用SVM方法进行沉积微相识别

作者针对目前沉积微相中的特征提取问题，提出了应用ＳＶＭ（支持向量机）方法进行沉积微相识别的方案。该方法不是象传统方法那样首先试图将原输入空间降维（即特征选择变换），而是设法将输入空间升维，以求在高维空间中问题变得线性可分（或接近线性可分）。因为升维后只是改变了内积运算，并没有使算法复杂性随着维数的增加而增加，因此这种方法才是可行的。所以。利用该方法更能胜任实际情况。实际处理表明该方法在小样本情况下

SUPPORT VECTOR MACHINE METHODS IN PATTERN RECOGNITION OF SEDIMENTARY FACIES

Aiming at the problem of pattern recognition in sedimentary facies analysis, we put forward a scheme which apply SVM to sedimentary facies recognition. Unlike traditional method try to reduce the dimension of input space(i.e. characters selection and transformation), SVM increase dimension of input space to ensure it is Linearly Separable in high dimension space. The method is feasible because it only changes inner product operation and the complexity of algorithm doesn't increase. Using SVM we needn't wasting time on character extraction but resort to the intrinsic character extraction ability which makes it more suitable in practical instance. The result of practical application indicates that the performance of SVM had superiority over RBFNN and overcome the problem of overfitting excellently.