The basic idea of SVM is to map linear inseparable input data into a high dimensional linear separable feature space via a nonlinear mapping technique ( kernel dot product), and classification or regression is done in the feature space. SVM的基本思想就是通过非线性内积核函数将线性不可分的低维空间数据映射到一个线性可分的高维特征空间,在这个特征空间中进行分类或回归拟合。
Considering that the data features were expected to be more separable in kernel space, we first performed the K-means clustering in kernel space, then trained the sub-class data separately using OC-SVMs and established a multiple hyperspheres classification model to decide the class label of new data. 算法利用核空间中样本特征差异突出的特性,首先对样本在核空间进行K-均值聚类,然后使用OC-SVMs对各子类训练建立多超球面分类模型,实现分类判决。
Energy equation governing the heat exchange is solved by Separable Kernel Method. 用可分核法求解能量控制方程。