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The support vector machine (SVM) is a common supervised learning technique for solving classification and regression problems.

It is mainly used in Machine Learning to solve classification problems.

The goal of the SVM algorithm is to find the best decision line or boundary to categorize the n-dimensional space into classes so that the machine can easily place subsequent data points into the correct category.

Hyperplanes are decision boundaries that help classify data points.

This line is called a hyperplane in N-dimensional space (N - the number of features), and this line distinguishes between data points.

Data points falling on either side of the hyperplane can be assigned to different classes.

SVM classification is used in face detection.

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