What are the different kernels in SVM?
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