# Support Vector Machine

Note: this post meant to help clarify the tutorial question number 2  for COMP 9417 – Week 9, School of Computer Science and Engineering, UNSW (s1 – 2017)

Support Vector Machine

Support Vector Machine (SVM) is essentially an approach to learning linear classifiers  which enables SVM to maximising the margin. Here is the picture, inspired by Flach – Fig. 7.6 – 7.7, that shows the difference between decision boundary produced by SVM, and other linear classifiers (such as: linear regression or perceptron).

To achieve that, SVM utilise below objective function, which attempts to find the values of $alpha_1,...,alpha_n$ that maximise the function.