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Constructing boosting algorithms from SVMs

An application to one-class classification
: Ratsch, G.; Mika, S.; Schölkopf, B.; Müller, K.-R.


IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (2002), No.9, pp.1184-1199
ISSN: 0162-8828
Journal Article
Fraunhofer FIRST ()

We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation procedure for a new algorithm-one-class leveraging-starting from the one-class support vector machine (1-SVM). This is a first step toward unsupervised learning in a boosting framework. Building on so-called barrier methods known from the theory of constrained optimization, it returns a function, written as a convex combination of base hypotheses, that characterizes whether a given test point is likely to have been generated from the distribution underlying the training data. Simulations on one-class classification problems demonstrate the usefulness of our approach.