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SVM—Support Vector Machines

• A relatively new classification method for both linear and nonlinear data
• It uses a nonlinear mapping to transform the original training data into a higher dimension
• With the new dimension, it searches for the linear optimal separating hyperplane (i.e., “decision boundary”)
• With an appropriate nonlinear mapping to a sufficiently high dimension, data from two classes can always be separated by a hyperplane
• SVM finds this hyperplane using support vectors (“essential” training tuples) and margins (defined by the support vectors)