Support vector machine: a support vector machine (svm) classification by tinding the hypern aximizes the margin between two classes. the vectors (cases) that de

Support Vector Machine: A Support vector machine (SVM) classification by Tinding the hypern aximizes the margin between two classes. The vectors (cases) that define the hyperplane are t vectors. Dinearty among the variabies are Support vector margin width 2 Algorithm: 1) Define an optimal hyperplane maximize margin. 2) Extend the above definition for non-linearly separable problems: have a penalty term mis-classification. Map data to high dimensional space where it is easier to classify with linear dec surfaces: reformulate problem so that data is mapped implicitly to this space. 3) To define an optimal hyperplane we need to maximize the width of the margin(w. X 2 max W.X+b 1 W.X+b 1 S.t. (w.x+b) 21 Vx of class1 w (w.x+b) S-1 Vx of class2