Polynomial, linear, non-linear, Radial Basis Function, etc. There are various types of kernel functions used in the SVM algorithm, i.e. Kernel trick is the function that transforms data into a suitable form. So the answer is no, to solve this problem, SVM has a technique that is commonly known as a kernel trick. But the question that arises here is should we add this feature of SVM to identify hyper-plane. In the SVM algorithm, it is easy to classify using linear hyperplane between two classes. In the above-mentioned plot, red circles are closed to the origin of the x-axis and y-axis, leading the value of z to lower, and star is exactly the opposite of the circle, it is away from the origin of the x-axis and y-axis, leading to the value of z to high.All the values on the z-axis should be positive because z is equaled to the sum of x squared and y squared.

Plots all data points on the x and z-axis. In this scenario, we are going to use this new feature z=x^2+y^2. To classify these classes, SVMВ introduces some additional features. In the below-mentioned image, we donвЂ™t have a linear hyper-plane between classes. Till now, we have looked at the linear hyper-plane. Scenario 5:В Fine hyper-plane to differentiate classes Because of the robustness property of the SVM algorithm, it will find the right hyperplane with a higher margin ignoring an outlier. For star class, this star is the outlier. Scenario 4:В Classify two classesДЄs you can see in the below-mentioned image, we are unable to differentiate two classes using a straight line because one star lies as an outlier in the other circle class. In this scenario, hyper-plane A has classified all accurately, and there is some error With the classification Of hyper-plane B. But in the SVM algorithm, it selects that hyper-plane which classify classes accurate prior to maximizing margin. Note: To identify the hyper-plane, follow the same rules as mentioned in the previous sections.ДЄs you can see in the above-mentioned image, the margin of hyper-plane B is higher than the margin of hyper-plane A thatвЂ™s why some will select hyper-plane B as a right. Scenario 3:В Identify the right hyper-plane Hence we chose hyperplane C with maximum margin because of robustness. So in this scenario, C is the right hyperplane.В If we choose the hyperplane with a minimum margin, it can lead to misclassification.

In the above-mentioned image, the margin of hyper-plane C is higher than the hyper-plane A and hyper-plane B. In this scenario, we increase the distance between the nearest data points to identify the right hyper-plane. These three hyper-planes are already differentiating classes very well. Here we have taken three hyper-planes, i.e. Scenario 2:В Identify the right hyper-plane In the above-mentioned image, hyper-plane B differentiates two classes very well. Select hyper-plane which differentiates two classes. To identify the right hyper-plane, we should know the thumb rule.

Svm hyperplan maximize distance