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Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
September 2002 (vol. 24 no. 9)
pp. 1184-1199

Abstract—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.

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Index Terms:
Boosting, SVMs, one-class classification, unsupervised learning, novelty detection.
Citation:
Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Klaus-Robert Müller, "Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1184-1199, Sept. 2002, doi:10.1109/TPAMI.2002.1033211
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