Rahul Khanna continues a series on support vector machines:

In this blog post, we will look at a detailed explanation of how to use SVM for complex decision boundaries and build Non-Linear Classifiers using SVM. The primary method for doing this is by using Kernels.

In linear SVM we find margin maximizing hyperplane with features Xi’s . Similarly, in Logistic regression, we also try to find the hyperplane which minimizes logistic loss with features Xi’s. Most often when we use both these techniques the results are the same. But linear SVM or for the same reason a logistic regression would fail where there is a need to have complex or non-linear decision boundaries. These types of boundaries are then achieved by SVM using Kernels. So let us understand how SVM creates non-linear boundaries using Kernels

Read on to see how it works.