Two Bayesian treatments of the n-tuple recognition method

Richard Rohwer

    Research output: Chapter in Book/Published conference outputChapter


    Two probabilistic interpretations of the n-tuple recognition method are put forward in order to allow this technique to be analysed with the same Bayesian methods used in connection with other neural network models. Elementary demonstrations are then given of the use of maximum likelihood and maximum entropy methods for tuning the model parameters and assisting their interpretation. One of the models can be used to illustrate the significance of overlapping n-tuple samples with respect to correlations in the patterns.
    Original languageEnglish
    Title of host publicationFourth International Conference on Artificial Neural Networks, 1995
    Place of PublicationCambridge
    Number of pages6
    ISBN (Print)0852966415
    Publication statusPublished - 26 Jun 1995
    EventProc. IEE 4th International Conf. on Artificial Neural Networks (publication 409) -
    Duration: 26 Jun 199526 Jun 1995

    Publication series

    NameIEE conference publication
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)


    ConferenceProc. IEE 4th International Conf. on Artificial Neural Networks (publication 409)

    Bibliographical note

    ©1995 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.


    • Bayes methods
    • maximum entropy methods
    • neural nets
    • pattern recognition
    • Bayesian treatments
    • maximum entropy
    • maximum likelihood
    • model parameters
    • n-tuple recognition
    • neural network models
    • probabilistic interpretations


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