So they have better performance than adaptive Gaussian kernel. 因而,相對於自適應高斯核函數而言,兩者性能更優。
As for the undivided linear sample space, the kernel function is needed to map onto another high dimension linear space. 對於線性不可分的樣本空間,需要尋找核函數,將線性不可分的樣本集映射到另一個高維線性空間。