Support Vector Machine

Support Vector Machine (SVM)

Support Vector Machine (a.k.a SVM) is one of the famous machine learning techniques. It is mainly used for supervised machine learning(classification) and regression analysis. It uses the concept of hyperplane for optimal classification.

• Suited for extreme cases ( Segregates two classes with a vector gap)
• Only support vectors are important, whereas other training examples are relatively unimportant
• Use support vectors to find hyperplane that maximize the margin between classified groups
• Depending on the circumstances, it can be linear or non-linear
• Kernel function can be used to transform non-linear into higher dimensional feature space to make linear separation possible. This is called kernel trick (see image below)