What advantage does Guassian kernel have than any other kernels, such as linear kernel, polynomial kernel and so on?
The advantange is that it is related to euclidean distance, and if your problem benefits from this type of feature, e.g. you have a highly non-linear problem where values nearby some vector point tend to be of a different class, it will be able to find relationships that other kernels would not, and create highly complex decision boundaries.