#### Support Vector Machines are a type of supervised machine learning algorithm that provides analysis of data for classification and regression analysis. While they can be used for regression, **SVM** is mostly used for classification. We carry out plotting in the n-dimensional space. Value of each feature is also the value of the specific coordinate. 1. Objective. In our previous Machine Learning blog we have discussed about **SVM** (**Support Vector Machine**) in Machine Learning. Now we are going to provide you a detailed description of **SVM** Kernel and Different Kernel Functions and its examples such as linear, nonlinear, polynomial, Gaussian kernel, Radial basis function (RBF), sigmoid etc.. If we search for a **dual** function, we could end up with the one at the bottom of the graph, whose maximum is the point D. In this case, we clearly see that D is a lower bound. We defined the value P − D and call it the duality gap. In this example, P − D > 0 and we say that weak duality holds.

**dual**Lagrangian formulation: the

**dual**problem for

**SVM**Where x are the features and y is the target value. y is defined as 1 for the positive class and -1 for the negative class. In this article, we will show the soft margin implementation of binary class linear

**SVM**by solving the

**dual**problem. Support-vector machine weights have also been used to interpret

**SVM**models in the past. Posthoc interpretation of support-vector machine models in order to identify features used by the model to make predictions is a relatively new area of research with special significance in the biological sciences. History. In the most pleasant settings, including

**SVM**, you get an even stronger guarantee, that the optimal solutions for the primal and

**dual**problems have equal objective value. That is, the bound that the

**dual**objective provides on the primal optimum is tight. In that case, the primal and

**dual**are two equivalent perspectives on the same problem.