Support vector mind map of wine speak

Brendan Flanagan, Sachio Hirokawa

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Models created by blackbox machine learning techniques such as SVM can be difficult to interpret. It is because these methods do not offer a clear explanation of how classifications are derived that is easy for humans to understand. Other machine learning techniques, such as: decision trees, produce models that are intuitive for humans to interpret. However, there are often cases where an SVM model will out preform a more intuitive model, making interpretation of SVM trained models an important problem. In this paper, we propose a method of visualizing linear SVM models for text classification by analyzing the relation of features in the support vectors. An example of this method is shown in a case study into the interpretation of a model trained on wine tasting notes.

    Original languageEnglish
    Title of host publicationHuman Interface and the Management of Information
    Subtitle of host publicationInformation, Design and Interaction - 18th International Conference, HCI International 2016, Proceedings
    EditorsSakae Yamamoto
    PublisherSpringer Verlag
    Number of pages9
    ISBN (Print)9783319403489
    Publication statusPublished - 2016
    Event18th International Conference on Human-Computer Interaction, HCI International 2016 - Toronto, Canada
    Duration: Jul 17 2016Jul 22 2016

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Other18th International Conference on Human-Computer Interaction, HCI International 2016

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)


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