Kernel methods for SVM map data to a higher dimensional space where a linear decision boundary can be found. This is done by using a kernel function, which computes the similarity between two data points in the original space. The kernel function can be linear, polynomial, or radial basis function. SVM with kernel methods has been shown to achieve state-of-the-art results on a variety of classification and regression tasks.
Which of the following is not a kernel function: linear kernel, polynomial kernel, radial basis function, Gaussian kernel?
What is the kernel trick?
Which of the following is not a type of support vector machine: linear SVM, polynomial SVM, radial basis function SVM, Gaussian SVM?
Previous