What is dual and primal in SVM?

This comes from the duality principle which states that optimization problems may be viewed as primal (in this case minimising over w and b) or dual (in this case, maximising over a). For a convex optimisation problem, the primal and dual have the same optimum solution.

What is primal in SVM?

PRIMAL FORM Let’s talk about the above optimization problem, it’s an optimization problem where we are trying to minimize (W and biases) such that alphas are maximized. Basically, it’s a MIN(MAX) problem where we are trying to minimize the product of W'(transpose) and W such that y_k*[W’*X_k + b] >= 1.

What is primal form of SVM?

PRIMAL FORM Basically, it’s a MIN(MAX) problem where we are trying to minimize the product of W'(transpose) and W such that y_k*[W’*X_k + b] >= 1. Before we look at the second equation, let’s talk about constraint optimization and normal optimization.

How to derive the primal formulation of SVM?

In order to derive the primal formulation of SVM, it is necessary to define an analytical formula to tell the distance between the data point and the hyperplane. It is obvious that any data point on the hyperplane satisfy the above equation. is a data point on the hyperplane).

What are the advantages of dual form in SVM?

One of the important advantage of using the dual form in SVM is that it allow us to apply kernels. Kernel search an optimal separating hyperplane in a higher dimensional space without increasing the computational complexity much. Kernel can be applied, if the algorithm takes the data points as input in terms of its inner product .

What is the primary objective of SVM?

It must be clear by now that primary objective of the SVM is to maximize the margin. In order to derive the primal formulation of SVM, it is necessary to define an analytical formula to tell the distance between the data point and the hyperplane.

Why is the separating hyperplane given by a SVM optimal?

The separating hyperplane given by a SVM is optimal because it observes separating hyper plane by maximizing the distance between the two classes on the training data. It solves the below optimization problem to find an optimal separating hyperplane. It is an optimization problem with an inequality constraint.