径向基函数核

此外，模型中还采用了多属性的特征提取方法对入侵行为进行特征提取；构造了以径向基函数为核函数的SVM分类器，提高了对未知入侵行为检测的准确性。

In addition , the method of multiple attributes is used for property abstraction of intrusions , and the RBF based SVM is used to construct the classifier which enhances the detection accuracy for unknown intrusions .

从中优选出了径向基函数为核函数的ε容错支持向量回归机作为该模型的原型。

Then , ε - insensitive SVM with radial basis function as kernel is adopted as prototype of this model .

通过比较三种不同核函数组成的分类器，得出采用径向基函数作为核函数的分类效果最好，准确率达到97.51%。

By comparing three Classifiers using three different kernel functions , the conclusion is that the classifier with radial basis function as kernel is the best , with the accuracy of 97.51 % .

对建模波段和数据预处理方法进行了优化，采用径向基函数为核函数，利用网格寻优算法计算支持向量回归模型的最优参数。

The range of wavenumber and the preprocessing methods were screened . The radial basis function was used as kernel function for SVR , the parameters of SVR were optimized by grid search technique .

重点讨论以径向基函数为代表的核函数参数确定问题。

The parameter selection of the kernel function represented by RBF function was researched as an emphasis .

运用径向基函数在不同的核函数情况下对非线性函数进行拟合，通过研究表明在本文样本数一定的情况下应用高斯函数作为核函数近似模型的精度最高。

The nonlinear function is fitted by using the radial basis function that is at the circumstances of different kernel functions .