Combining spatially distributed predictions from neural networks


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

Divisions: Aston University (General)
Uncontrolled Keywords: spatially-distributed,neural network,Hidden Markov modelling
ISBN: NCRG/97/026
PURE Output Type: Technical report
Published Date: 1997
Authors: Williams, Christopher K. I.

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