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Analytical Results on Style-Constrained Bayesian Classification of Pattern Fields
July 2007 (vol. 29 no. 7)
pp. 1280-1285
We formalize the notion of style context, which accounts for the increased accuracy of the field classifiers reported in this journal recently. We argue that style context forms the basis of all order-independent field classification schemes. We distinguish between intraclass style, which underlies most adaptive classifiers, and interclass style, which is a manifestation of interpattern dependence between the features of the patterns of a field. We show how style-constrained classifiers can be optimized either for field error (useful for short fields like zip codes) or for singlet error (for long fields, like business letters). We derive bounds on the reduction of error rate with field length and show that the error rate of the optimal style-constrained field classifier converges asymptotically to the error rate of a style-aware Bayesian singlet classifier.
Index Terms:
Style context, field classification, adaptive classification, Bayesian classification.
Citation:
Sriharsha Veeramachaneni, George Nagy, "Analytical Results on Style-Constrained Bayesian Classification of Pattern Fields," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 7, pp. 1280-1285, July 2007, doi:10.1109/TPAMI.2007.1030
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