Answer / Anurag Kumar

Kernel SVM is a variant of support vector machines that can handle non-linearly separable data using a technique called the kernel trick. The kernel function maps the input data into a higher-dimensional space where it becomes linearly separable, and then finds the optimal hyperplane in this space. Commonly used kernels include linear, polynomial, radial basis function (RBF), and sigmoid.

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