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A. L. Girard
ABSTRACT
This paper discusses learning in pattern recognition in terms of parameter estimation. Methods of Estimation Theory are used to show that reinforcement learning is implemented by sequential parameter estimation which alters both a priori and spontaneously learned templates feature by feature. Spontaneous learning is handled as a problem in decision theory and optimum thresholds for learning or classification are developed in terms of Bayesian thresholds and decision statistics based on the mutual information of observables and templates. 
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
Parameter estimation and learning/classification threshold optimization applied to maxentropic adaptive pattern recognitionSite generated on Friday, 06 January 2006