Machine Learning Guide for Oil and Gas Using Python

Support vector machine

SVM is another form of a supervised ML algorithm that can be used for both classification and regression problems. As opposed to many of the ML algorithms where the objective function is to minimize a cost function, the objective in SVM is to maximize the margin between support vectors through a separating hyperplane (Cortes & Vapnik, 1995). In Fig. 5.29, a separating hyperplane (solid black line) is drawn to separate blue instances from the red ones. This hyperplane is drawn in a manner to maximize the margin from both sides. Please note that the widest possible stretch must be drawn to separate the red and blue instances. The closest blue and red instances to the separating hyperplane are referred to as support vectors ...