Machine Learning using Hyperkernels

Cheng Soon Ong*, Alexander J. Smola

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    25 Citations (Scopus)

    Abstract

    We expand on the problem of learning a kernel via a RKHS on the space of kernels itself. The resulting optimization problem is shown to have a semidefinite programming solution. We demonstrate that it is possible to learn the kernel for various formulations of machine learning problems. Specifically, we provide mathematical programming formulations and experimental results for the C-SVM, v-SVM and Lagrangian SVM for classification on UCI data, and novelty detection.

    Original languageEnglish
    Title of host publicationProceedings, Twentieth International Conference on Machine Learning
    EditorsT. Fawcett, N. Mishra
    Pages568-575
    Number of pages8
    Publication statusPublished - 2003
    EventProceedings, Twentieth International Conference on Machine Learning - Washington, DC, United States
    Duration: 21 Aug 200324 Aug 2003

    Publication series

    NameProceedings, Twentieth International Conference on Machine Learning
    Volume2

    Conference

    ConferenceProceedings, Twentieth International Conference on Machine Learning
    Country/TerritoryUnited States
    CityWashington, DC
    Period21/08/0324/08/03

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