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J. Payne and M. Day
ABSTRACT
This paper presents a projection filter for use with parameterised learning models. Two aspects of the projection filter are considered. Firstly, the filter's operation is demonstrated using the perceptron learning rule on a simple two class discrimination problem. Secondly, the projection filter is used to extend the learning capabilities of a nonlinear spatio-temporal neural network model. An experiment was undertaken to compare the effectiveness of applying temporal backpropagation to a multilayer feedforward network employing either finite impulse response (FIR) or projection filters. Results show that the projection filter reached a lower mean square error (MSE) when compared to the FIR version of the network 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
A Projection Filter for Use with Parameterised Learning ModelsSite generated on Friday, 06 January 2006