Support Vector Machine

A support vector machine, or an SVM, is a machine learning technique widely used in the fields of data analysis and pattern recognition. Classifying data has been one of the major tasks in machine learning problems. The idea of SVM is to build a hyperplane between datasets to classify the data under different labels. There could be more than one hyperplane dividing the data space for making the classification. If a line passes too close to the points, it will be noise sensitive and it will not generalize correctly.

Given a particular hyperplane, we can compute the distance between the hyperplane and the closest data point. A good separation is achieved by the hyperplane that has the largest distance to the nearest training-data point of any class, since in general the larger the margin the lower the error of the classifier. Therefore, our goal should be to find the line passing as far as possible from all points. SVM finds an optimal solution, by maximizing the margin around the separating hyperplanes.