Combining spatially distributed predictions from neural networks

Christopher K. I. Williams

    Research output: Preprint or Working paperTechnical report


    In this report we discuss the problem of combining spatially-distributed predictions from neural networks. An example of this problem is the prediction of a wind vector-field from remote-sensing data by combining bottom-up predictions (wind vector predictions on a pixel-by-pixel basis) with prior knowledge about wind-field configurations. This task can be achieved using the scaled-likelihood method, which has been used by Morgan and Bourlard (1995) and Smyth (1994), in the context of Hidden Markov modelling
    Original languageEnglish
    Place of PublicationBirmingham B4 7ET, UK
    PublisherAston University
    Number of pages4
    ISBN (Print)NCRG/97/026
    Publication statusPublished - 1997


    • spatially-distributed
    • neural network
    • Hidden Markov modelling


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