Frequency coding: an effective method for combining dichotomizers

Srinivas Andra; George Nagy; Cheng-Lin Liu

doi:10.1117/12.708803

29 January 2007 Frequency coding: an effective method for combining dichotomizers

Srinivas Andra, George Nagy, Cheng-Lin Liu

Proceedings Volume 6500, Document Recognition and Retrieval XIV; 650004 (2007) https://doi.org/10.1117/12.708803
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

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.

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Srinivas Andra, George Nagy, and Cheng-Lin Liu "Frequency coding: an effective method for combining dichotomizers", Proc. SPIE 6500, Document Recognition and Retrieval XIV, 650004 (29 January 2007); https://doi.org/10.1117/12.708803

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KEYWORDS

Binary data

Image classification

Error analysis

Matrices

Statistical analysis

Algorithm development

Calibration

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