Kernel is used due to a series of mathematical functions used in the Support Vector Machines giving the window to manipulate the data. There are some different types of kernels in SVM.

S.no Types Description
1. Polynomial kernel The polynomial kernel is defined as; b = degree of kernel & a = constant term. in the polynomial kernel, we easily calculate the dot item by increasing the capacity of the kernel.
2. Gaussian kernel Gaussian kernel changes the dot item in the infinite-dimensional space into the Gaussian function of the space between points in the data space.
3. Gaussian radial basis function(RBF) RBF kernel is a function whose worth depends on the extent of the origin or from some point.
4. Laplace RBF kernel It is a general-purpose kernel, and is used when there is no prior knowledge about data.
5. Hyperbolic tangent kernel This kernel can be used in neural networks.
6. Sigmoid kernel Its basically a proxy for neural networks.
7. Bessel function of the first kind kernel We use it to erase the cross term in mathematical functions.
8. ANOVA radial basis kernel It can use in regression problems.
9. Linear splines kernel in one dimension It is helpful when dealing with huge sparse data vectors. It is frequently used in text categorization.
BY Best Interview Question ON 30 Dec 2023