An adaptive approximation for Gaussian wavelet kernel

Young Mok Ha, Ji Won Yoon

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


    Kernel machine plays a critical role in science community since temporal data become more important and popular with rapidly increasing big data analysis. A major problem for the machine is difficulty in constructing kernel function. We show that it is possible to adaptively estimate the parameters of Gaussian wavelet kernel in Laplace method. Our approach is constructed on an obvious fact that the gradient of the kernel with respect to a central variable of feature space becomes zero. It is remarkable that the complexity of our estimation method is O(N) for N data. In order to validate the performance of the proposed approach, we simulate two kernel regression models which exploit the proposed approach on real electricity load data from Korea power exchange and electricity consumption data from Ireland's Commission for Energy Regulation.

    Original languageEnglish
    Title of host publicationInternational Conference on Advanced Communication Technology, ICACT
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages5
    ISBN (Print)9788996865063
    Publication statusPublished - 2016 Mar 1
    Event18th International Conference on Advanced Communications Technology, ICACT 2016 - Pyeongchang, Korea, Republic of
    Duration: 2016 Jan 312016 Feb 3


    Other18th International Conference on Advanced Communications Technology, ICACT 2016
    Country/TerritoryKorea, Republic of


    • adaptive inference
    • electricity load/consumption forecast
    • Gaussian wavelet kernel
    • Laplace approximation

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering


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