In Support Vector Machines (SVMs), a kernel function is used to transform the input data into a higher-dimensional feature space, which makes it easier to distinguish between classes or generate predictions. The kernel function is a set of mathematical functions used in SVM that provides the window to manipulate the data. The kernel function generally transforms the training set of data so that a non-linear decision surface is able to transform to a linear equation in a higher number of dimension spaces. SVM algorithms use different types of kernel functions, including linear, nonlinear, polynomial, Gaussian kernel, Radial basis function (RBF), sigmoid, and others. The choice of kernel function depends on the specific problem and the characteristics of the data. Some commonly used kernel functions in SVMs include:
- Linear Kernel: used when the data is linearly separable.
- Polynomial Kernel: a nonlinear kernel function that employs polynomial functions to transfer the input data into a higher-dimensional feature space.
- Gaussian Kernel: a general-purpose kernel used when there is no prior knowledge about the data.
- Radial Basis Function (RBF): a general-purpose kernel used when there is no prior knowledge about the data.
The kernel trick is employed to empower the linear classifier, enabling the SVM to solve a larger class of problems. The kernel method is generally used in SVMs where the algorithms classify data by finding the hyperplane that separates the data points of different classes. The most important benefit of the kernel method is that it can work with non-linearly separable data, and it works with multiple kernel functions depending on the type of data.
