![]() The value of class label here can be only either be -1 or +1 (for 2-class problem). Now, consider the training D such that where represents the n-dimesnsional data point and class label respectively. Now since all the plane x in the hyperplane should satisfy the following equation: Here b is used to select the hyperplane i.e perpendicular to the normal vector. These are commonly referred to as the weight vector in machine learning. Below is the method to calculate linearly separable hyperplane.Ī separating hyperplane can be defined by two terms: an intercept term called b and a decision hyperplane normal vector called w. Generally, the margin can be taken as 2* p, where p is the distance b/w separating hyperplane and nearest support vector. If an (n x n) diagonal matrix has all unit elements on the diagonal (aii. Thus, the best hyperplane will be whose margin is the maximum. A matrix of dimensions (m x n), with m and n positive integers, is an array of. This distance b/w separating hyperplanes and support vector known as margin. The idea behind that this hyperplane should farthest from the support vectors. Before talking about hyperplane arrangements, let us start with individual hyperplanes. Now, we understand the hyperplane, we also need to find the most optimized hyperplane.
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