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Co-regularised support vector regression

Ullrich, Katrin; Kamp, M.; Gärtner, Thomas; Vogt, Martin; Wrobel, Stefan

Authors

Katrin Ullrich

M. Kamp

Thomas Gärtner

Martin Vogt

Stefan Wrobel



Abstract

We consider a semi-supervised learning scenario for regression, where only few labelled examples, many unlabelled instances and different data representations (multiple views) are available. For this setting, we extend support vector regression with a co-regularisation term and obtain co-regularised support vector regression (CoSVR). In addition to labelled data, co-regularisation includes information from unlabelled examples by ensuring that models trained on different views make similar predictions. Ligand affinity prediction is an important real-world problem that fits into this scenario. The characterisation of the strength of protein-ligand bonds is a crucial step in the process of drug discovery and design. We introduce variants of the base CoSVR algorithm and discuss their theoretical and computational properties. For the CoSVR function class we provide a theoretical bound on the Rademacher complexity. Finally, we demonstrate the usefulness of CoSVR for the affinity prediction task and evaluate its performance empirically on different protein-ligand datasets. We show that CoSVR outperforms co-regularised least squares regression as well as existing state-of-the-art approaches for affinity prediction.

Citation

Ullrich, K., Kamp, M., Gärtner, T., Vogt, M., & Wrobel, S. (2017). Co-regularised support vector regression

Conference Name The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2017
End Date Sep 22, 2017
Acceptance Date Jun 22, 2017
Publication Date Sep 19, 2017
Deposit Date Aug 22, 2017
Publicly Available Date Sep 19, 2017
Peer Reviewed Peer Reviewed
Keywords regression, kernel methods, semi-supervised learning, multiple views,
co-regularisation, Rademacher complexity, ligand affinity prediction
Public URL http://eprints.nottingham.ac.uk/id/eprint/45044
Related Public URLs http://ecmlpkdd2017.ijs.si/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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