The optimal parameters of mathematics model were studied using leave-one-out cross validation method.
利用内部交叉验证和自动优化功能对预测模型进行了优化，确定了最优建模参数。
In this paper, soft sensor modeling method based on Least Square SVM (LS SVM) is proposed, and cross validation method is used to select hyper-parameter of LS SVM model.
本文研究了基于最小二乘支持向量机的软测量建模方法，并用交叉验证的方法进行支持向量机参数选择。
In the first block, a modified generalized Cross validation blur identification method was proposed.
在第一部分，提出了一种改进的广义交叉验证模糊辨识方法。
The proposed method includes the GPR multi-object recognition method based on one against one SVM, the parameter selection method based on the cross-validation and the multichannel recognition method.
所提方法包括基于一对一的探地雷达多目标识别方法、交叉验证的参数选取方法、多通道识别方法；
Lastly, the validity of the model is also testified by the cross-validation method.
最后，利用交叉检验法验证该模型的正确性。
We give the concept of the leave-one-out cross-validation score. We also discuss the best method to choose the bandwidth and give the best value of it.
给出了缺一交叉验证得分的概念，讨论了最优带宽的理论的和实际的选择方法，并得出实际最优带宽的值。
Then, parameters selection method of LS-SVR was proposed based on adaptive immune algorithm (AIA) plus 5-fold cross validation, which was applied to inverse kinematics modeling of 2DOF robot.
并将其应用于两自由度机器人的逆运动学建模中，仿真结果表明了该方法的有效性。
Then, parameters selection method of LS-SVR was proposed based on adaptive immune algorithm (AIA) plus 5-fold cross validation, which was applied to inverse kinematics modeling of 2DOF robot.
并将其应用于两自由度机器人的逆运动学建模中，仿真结果表明了该方法的有效性。
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