In this post, we will look at a detailed explanation of how to use SVM for complex decision boundaries and build Non-Linear Classifiers using SVM.
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In our previous blogs, we have already looked at and gotten a higher level of understanding of SVM and why we should choose SVM over other classifiers.
In this 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 fails for the same reason a logistic regression would fail; 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’s understand how SVM creates non-linear boundaries using Kernels.
First, try to pick a few points on the feature plane and call them landmarks. Then, we try to compute new features for an example(X) depending on the closeness of these features to the landmarks.
