PROJECT TITLE :

A Stochastic Approach for Finding Optimal Context in a Contextual Pattern Analysis Task

ABSTRACT:

This text issues contextual pattern analysis tasks. As different contexts offer totally different performances, models for finding the optimal context are revisited here. Random field models for the input information are assumed. An underlying random field is represented by a collection of parameters capturing the spatial dependence. Next, a Bayesian approach is revisited to develop a call rule for choosing acceptable context. The relevance of this approach is explored for three pattern analysis tasks, particularly, handwriting analysis, image compression, and word sense disambiguation.


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