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Nomograms for Visualizing Support Vector Machines

Aleks Jakulin and Martin Možina and Janez Demšar and Ivan Bratko and Blaz Zupan (2005) Nomograms for Visualizing Support Vector Machines. In: SIGKDD'05 Chicago, August 2005, Illinois, USA.

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    We propose a simple yet potentially very effective way of visualizing trained support vector machines. Nomograms are an established model visualization technique that can graphically encode the complete model on a single page. The dimensionality of the visualization does not depend on the number of attributes, but merely on the properties of the kernel. To represent the effect of each predictive feature on the log odds ratio scale as required for the nomograms, we employ logistic regression to convert the distance from the separating hyperplane into a probability. Case studies on selected data sets show that for a technique thought to be a black-box, nomograms can clearly expose its internal structure. By providing an easy-to-interpret visualization the analysts can gain insight and study the effects of predictive factors.

    Item Type: Conference or Workshop Item (Paper)
    Keywords: nomogram, visualization, support vector machines, machine learning
    Language of Content: English
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    http://portal.acm.org/citation.cfm?id=1081886&dl=GUIDE&coll=GUIDEAlternative location
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Divisions: Faculty of Computer and Information Science > Artificial Intelligence Laboratory
    Item ID: 214
    Date Deposited: 09 Jun 2006
    Last Modified: 13 Aug 2011 00:32
    URI: http://eprints.fri.uni-lj.si/id/eprint/214

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