Prof. Dr.

Wrobel, Stefan

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  • Publication
    Co-regularised support vector regression
    ( 2017)
    Ullrich, Katrin
    ;
    Kamp, Michael
    ;
    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. Code and data related to this chapter are available at: https://doi.org/10.6084/m9.figshare.5427241.
  • Publication
    Context-based clustering of image search results
    ( 2009)
    Wang, H.
    ;
    Missura, Olana
    ;
    Gärtner, Thomas
    ;
    Wrobel, Stefan
    In this work we propose to cluster image search results based on the textual contents of the referring webpages. The natural ambiguity and context-dependence of human languages lead to problems that plague modern image search engines: A user formulating a query usually has in mind just one topic, while the results produced to satisfy this query may (and usually do) belong to the different topics. Therefore, only part of the search results are relevant for a user. One of the possible ways to improve the user's experience is to cluster the results according to the topics they belong to and present the clustered results to the user. As opposed to the clustering based on visual features, an approach utilising the text information in the webpages containing the image is less computationally intensive and provides the resulting clusters with semantically meaningful names.