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dc.contributor.authorKhatami, Mohammaden_US
dc.contributor.authorSchultz, Thomasen_US
dc.contributor.editorSchulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michaelen_US
dc.description.abstractKernel Support Vector Machines (SVMs) are widely used for supervised classification, and have achieved state-of-the-art performance in numerous applications. We aim to further increase their efficacy by allowing a human operator to steer their training process. To this end, we identify several possible strategies for meaningful human intervention in their training, propose a corresponding visual analytics workflow, and implement it in a prototype system. Initial results from two users, on data from three different domains suggest that, in addition to facilitating better insight into the data and into the classifier's decision process, visual analytics can increase the efficacy of Support Vector Machines when the data available for training has a low number of samples, is unbalanced with respect to the different classes, contains outliers, irrelevant features, or mislabeled samples. However, we also discuss some limitations of improving the efficacy of supervised classification with visual analytics.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectVisualization application domains
dc.subjectVisual analytics
dc.subjectKernel methods
dc.subjectSupport vector machines
dc.titleOpen-Box Training of Kernel Support Vector Machines: Opportunities and Limitationsen_US
dc.description.seriesinformationVision, Modeling and Visualization
dc.description.sectionheadersMachine Learning in Vision and Analysis

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  • VMV19
    ISBN 978-3-03868-098-7

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