Maximum margin classification with support vector machines

Another powerful and widely used learning algorithm is the Support Vector Machine (SVM), which can be considered an extension of the perceptron. Using the perceptron algorithm, we minimized misclassification errors. However, in SVMs our optimization objective is to maximize the margin. The margin is defined as the distance between the separating hyperplane (decision boundary) and the training samples that are closest to this hyperplane, which are the so-called support vectors. This is illustrated in the following figure:

Maximum margin classification with support vector machines

Maximum margin intuition

The rationale behind having decision boundaries ...

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