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Proceedings Paper

Frequency coding: an effective method for combining dichotomizers
Author(s): Srinivas Andra; George Nagy; Cheng-Lin Liu
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Paper Abstract

Binary classifiers (dichotomizers) are combined for multi-class classification. Each region formed by the pairwise decision boundaries is assigned to the class with the highest frequency of training samples in that region. With more samples and classifiers, the frequencies converge to increasingly accurate non-parametric estimates of the posterior class probabilities in the vicinity of the decision boundaries. The method is applicable to non-parametric discrete or continuous class distributions dichotomized by either linear or non-linear classifiers (like support vector machines). We present a formal description of the method and place it in context with related methods. We present experimental results on machine-printed and handwritten digits that demonstrate the viability of frequency coding in a classification task.

Paper Details

Date Published: 29 January 2007
PDF: 8 pages
Proc. SPIE 6500, Document Recognition and Retrieval XIV, 650004 (29 January 2007); doi: 10.1117/12.708803
Show Author Affiliations
Srinivas Andra, Rensselaer Polytechnic Institute (United States)
George Nagy, Rensselaer Polytechnic Institute (United States)
Cheng-Lin Liu, Institute of Automation (China)


Published in SPIE Proceedings Vol. 6500:
Document Recognition and Retrieval XIV
Xiaofan Lin; Berrin A. Yanikoglu, Editor(s)

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