Co-regularised support vector regression
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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
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2017
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| Online Access: | https://eprints.nottingham.ac.uk/45044/ |
| _version_ | 1848797055793758208 |
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| author | Ullrich, Katrin Kamp, M. Gärtner, Thomas Vogt, Martin Wrobel, Stefan |
| author_facet | Ullrich, Katrin Kamp, M. Gärtner, Thomas Vogt, Martin Wrobel, Stefan |
| author_sort | Ullrich, Katrin |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:57:48Z |
| format | Conference or Workshop Item |
| id | nottingham-45044 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:57:48Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-450442020-05-04T19:07:23Z https://eprints.nottingham.ac.uk/45044/ Co-regularised support vector regression Ullrich, Katrin Kamp, M. Gärtner, Thomas Vogt, Martin Wrobel, Stefan 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. 2017-09-19 Conference or Workshop Item PeerReviewed Ullrich, Katrin, Kamp, M., Gärtner, Thomas, Vogt, Martin and Wrobel, Stefan (2017) Co-regularised support vector regression. In: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2017, 18-22 Sep 2017, Skopje, Macedonia. regression kernel methods semi-supervised learning multiple views co-regularisation Rademacher complexity ligand affinity prediction |
| spellingShingle | regression kernel methods semi-supervised learning multiple views co-regularisation Rademacher complexity ligand affinity prediction Ullrich, Katrin Kamp, M. Gärtner, Thomas Vogt, Martin Wrobel, Stefan Co-regularised support vector regression |
| title | Co-regularised support vector regression |
| title_full | Co-regularised support vector regression |
| title_fullStr | Co-regularised support vector regression |
| title_full_unstemmed | Co-regularised support vector regression |
| title_short | Co-regularised support vector regression |
| title_sort | co-regularised support vector regression |
| topic | regression kernel methods semi-supervised learning multiple views co-regularisation Rademacher complexity ligand affinity prediction |
| url | https://eprints.nottingham.ac.uk/45044/ |