Radial Basis Functions (RBFs) are set of functions which have same value at a fixed distance from a given central point. Even Gaussian Kernels with a covariance matrix which is diagonal and with constant variance will be radial in nature.
In SVMs, RBF Kernal and Gaussian Kernal are used interchangeably. But right way to specify is “Gaussian Radial Basis Function” because there can be other RBFs. Gaussian RBFs are one of the most used Kernal in SVMs. It can take data to infinite dimensional space and has infinite VC dimensions. One has to be careful to avoid overfitting when using Gaussian RBF Kernels. Read this paper for more information: http://www.cmap.polytechnique.fr...
向基函數（RBF）是一組函數，它們在距給定中心點的固定距離處具有相同的值。甚至具有協方差矩陣的高斯核也是徑向的，該協方差矩陣是對角的並且具有恆定的方差。
在SVM中，RBF Kernal和Gaussian Kernal可互換使用。但正確的指定方式是“高斯徑向基函數”，因為可以有其他RBF。高斯RBF是SVM中最常用的Kernal之一。它可以將數據帶入無限維空間並具有無限的VC維度。在使用高斯RBF內核時，必須小心避免過度擬合。更多信息：http：//www.cmap.polytechnique.fr ...