Learning using privileged information: SVM+ and weighted SVM |
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| Affiliation: | 1. Max Planck Institute for Informatics, Saarbrücken, Germany;2. Saarland University, Saarbrücken, Germany;1. Faculty of Mathematics and Computer Science, Hubei University, Wuhan 430062, China;2. Institute for Information and System Science, Xi’an Jiaotong University, Xi’an 710049, China;3. Faculty of Science and Technology, University of Macau, China;1. College of science, Tianjin University of Technology and Education, Tianjin 300222, China;2. Department of Dynamics and Control, Beihang University, Beijing 100191, China;3. Department of Electronic Engineering, City University of Hong Kong, Hong Kong Special Administrative Region |
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| 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. |
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| Keywords: | SVM SVM+ Weighted SVM Importance weighting Privileged information Prior knowledge |
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