基于模式识别理论的卡钻类型判别分析

将模式识别理论中的支持向量机、Bayes判别分析以及多元回归分析应用于卡钻类型判别分析，建立了基于模式识别理论的卡钻判别模型。以近几年川东北卡钻数据为例进行了算例分析，结果表明：采用支持向量机、Bayes判别法及多元回归法对卡钻类型判别的结果与实际结果的误判率分别为1.92%，11.52%，61.54%。支持向量机（SVM）判别结果精度最高，但其判别方程式较为复杂，不能直观看出各分量对结果的影响程度；多元回归分析判别方程形式简单，可以直观表达各参量与卡钻之间的密切程度，但其判别精度较低；Bayes判别法计算精度介于二者之间，但其判别精度与判别式的个数密切相关。

Discriminant Analysis on Sticking Based on Pattern Recognition Theory

The Support Vector Machine (SVM), Bayes discriminant analysis and multiple regression analysis were used for diagnosis and prediction of sticking. The discriminant model of stick type was built based on pattern recognition theory. The calculation analysis on the sticking data of northeastern Sichuan was made, it was indicated that the misjudgment rate for discriminant result of SVM, Bayes discriminant analysis and multiple regression analysis was 1.92%, 11.52% and 61.54%. The accuracy of SVM recognition result was the highest, but its discriminant equation is complex and the contribution of each component to the result could not be intuitively seen; while the equation of multiple regression analysis is simple, which could intuitively show the close degree between each component and sticking, but the accuracy of recognition result was lower. The accuracy of Bayes discriminant analysis was between the above two, but the discriminant accuracy is closely related to the number of discriminant.