Effects of norms on learning properties of support vector machines

Kazushi Ikeda, Noboru Murata

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

    1 Citation (Scopus)

    Abstract

    Support Vector Machines (SVMs) are known to have a high generalization ability, yet a heavy computational load since margin maximization results in a quadratic programming problem. It is known that this maximization task results in a pth-order programming problem if we employ the Lp norm instead of the L2 norm. In this paper, we theoretically show the effects of p on the learning properties of SVMs by clarifying its geometrical meaning.

    Original languageEnglish
    Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    VolumeV
    ISBN (Print)0780388747, 9780780388741
    DOIs
    Publication statusPublished - 2005
    Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA
    Duration: 2005 Mar 182005 Mar 23

    Other

    Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
    CityPhiladelphia, PA
    Period05/3/1805/3/23

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    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Signal Processing
    • Acoustics and Ultrasonics

    Cite this

    Ikeda, K., & Murata, N. (2005). Effects of norms on learning properties of support vector machines. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. V). [1416285] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2005.1416285