Support vector machines are among the most powerful and practical machine learning algorithms ever designed. They can be used to predict outcomes of marketing campaigns, predict user behavior on websites, accurately predict results in medical tests, and much more. In this article, we’re going to focus on the concept of a primal support vector machine (SVM) in detail including its math and crazy derivations. So let's get into it.
What is a Primal Support Vector Machine?
The thing you need to understand is that SVM is so powerful that it can even be used in more than 2-dimensions. In some cases, the data points encountered in a problem can be linearly separable or non-linearly separable,
If the data points of classes are linearly separable, we can simply formulate the optimization function using the basic SVM which is known as the Primal formulation of SVM. But when the data points are not linearly separable the Primal formulation simply doesn't work, Here we need to use something known as the Dual Form of SVM that deals with multiple dimensions. But in this article, we are focusing on the basic concept of SVM, ie, the Primal formulation of SVM.
Some important SVM terms