Chapter 10 Support Vector Machines
Support Vector Machines (SVM) is a classification model that maps observations as points in space so that the categories are divided by as wide a gap as possible. New observations can then be mapped into the space for prediction. The SVM algorithm finds the optimal separating hyperplane using a nonlinear mapping to a sufficiently high dimension. The hyperplane is defined by the observations that lie within a margin optimized by a cost hyperparameter. These observations are called the support vectors.
SVM is an extension of the support vector classifier which in turn is a generalization of the simple and intuitive maximal margin classifier. The best way to understand the SVM is to start with the maximal margin classifier and work up.
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