Unsupervised Learning and Generalization

Lars Kai Hansen, Jan Larsen

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    Abstract

    The concept of generalization is defined for a general class of unsupervised learning machines. The generalization error is a straightforward extension of the corresponding concept for supervised learning, and may be estimated empirically using a test set or by statistical means-in close analogy with supervised learning. The empirical and analytical estimates are compared for principal component analysis and for K-means clustering based density estimation
    Original languageEnglish
    Title of host publicationProceedings of IEEE International Conference on Neural Networks
    PublisherIEEE
    Publication date1996
    Pages25-30
    ISBN (Print)0-7803-3210-5
    DOIs
    Publication statusPublished - 1996
    EventIEEE International Conference on Neural Networks - Washington DC
    Duration: 1 Jan 1996 → …

    Conference

    ConferenceIEEE International Conference on Neural Networks
    CityWashington DC
    Period01/01/1996 → …

    Bibliographical note

    Copyright: 1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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