title: Learning using privileged information: SVM+ and weighted SVM.
abstract: Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additional information available only at training time-a framework implemented by SVM+. We relate the privileged information to importance weighting and show that the prior knowledge expressible with privileged features can also be encoded by weights associated with every training example. We show that a weighted SVM can always replicate an SVM+ solution, while the converse is not true and we construct a counterexample highlighting the limitations of SVM+. Finally, we touch on the problem of choosing weights for weighted SVMs when privileged features are not available.
authors.affiliation: Max Planck Institute for Informatics, Saarbrücken, Germany. Electronic address: mlapin@mpi-inf.mpg.de.
authors.forename: Maksim
authors.initials: M
authors.lastname: Lapin
authors.affiliation: Saarland University, Saarbrücken, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Hein
authors.affiliation: Max Planck Institute for Informatics, Saarbrücken, Germany.
authors.forename: Bernt
authors.initials: B
authors.lastname: Schiele
mesh_terms: D000465:Algorithms; D006801:Humans; D060388:Support Vector Machine
keywords: Importance weighting; Prior knowledge; Privileged information; SVM; SVM+; Weighted SVM