Abstract
We review some of the basic ideas of Support Vector Machines (SVM’s) for classification, with the goal of describing how these ideas can sit comfortably inside the statistical literature in decision theory and penalized likelihood regression. We review recent work on adaptive tuning of SVMs, discussing generalizations to the nonstandard case where the training set is not representative and misclassification costs are not equal. Mention is made of recent results in the multicategory case.
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