Increasing efficiency of SVM by adaptively penalizing outliers

Yiqiang Zhan, Dinggang Shen

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

    4 Citations (Scopus)

    Abstract

    In this paper, a novel training method is proposed to increase the classification efficiency of support vector machine (SVM). The efficiency of the SVM is determined by the number of support vectors, which is usually large for representing a highly convoluted separation hypersurface. We noted that the separation hypersurface is made unnecessarily over-convoluted around extreme outliers, which dominate the objective function of SVM. To suppress the domination from extreme outliers and thus relatively simplify the shape of separation hypersurface, we propose a method of adaptively penalizing the outliers in the objective function. Since our reformulated objective function has the similar format of the standard SVM, the idea of the existing SVM training algorithms is borrowed for training the proposed SVM. Our proposed method has been tested on the UCI machine learning repository, as well as a real clinical problem, i.e., tissue classification in prostate ultrasound images. Experimental results show that our method is able to dramatically increase the classification efficiency of the SVM, without losing its generalization ability.

    Original languageEnglish
    Title of host publicationEnergy Minimization Methods in Computer Vision and Pattern Recognition - 5th International Workshop, EMMCVPR 2005, Proceedings
    Pages539-551
    Number of pages13
    DOIs
    Publication statusPublished - 2005
    Event5th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2005 - St. Augustine, FL, United States
    Duration: 2005 Nov 92005 Nov 11

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3757 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other5th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2005
    Country/TerritoryUnited States
    CitySt. Augustine, FL
    Period05/11/905/11/11

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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