On-line probabilistic classification with particle filters

Pedro Højen-Sørensen, N. de Freitas, Torben L. Fog

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    Abstract

    We apply particle filters to the problem of on-line classification with possibly overlapping classes. This allows us to compute the probabilities of class membership as the classes evolve. Although we adopt neural network classifiers, the work can be extended to any other parametric classification scheme. We demonstrate our methodology on a simple example and on the problem of fault detection of dynamically operated marine diesel engines.
    Original languageEnglish
    Title of host publicationProceedings of the 2000 IEEE Signal Processing Society Workshop Neural Networks for Signal Processing X, 2000.
    PublisherIEEE
    Publication date2000
    ISBN (Print)0-7803-6278-0
    DOIs
    Publication statusPublished - 2000
    EventIEEE Signal Processing Society Workshop Neural Networks for Signal Processing X, 2000. -
    Duration: 1 Jan 2000 → …

    Conference

    ConferenceIEEE Signal Processing Society Workshop Neural Networks for Signal Processing X, 2000.
    Period01/01/2000 → …

    Bibliographical note

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