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.
Exploiting style consistency in groups of patterns (pattern fields)
generated by the same source has been demonstrated to yield higher
accuracies in OCR applications. The accuracy gains obtained by a
style consistent classifier depend on the amount of style in a
dataset in addition to the classifier itself. The computational
complexity of style-based classifiers precludes their applicability
in situations where datasets have small amounts of style. In this
paper, we propose a correlation-based measure to quantify the amount
of style in a dataset and demonstrate its use in determining the
suitability of a style consistent classifier on both simulation and
Most pattern classifiers are trained on data from multiple sources,
so that they can accurately classify data from any source. However,
in many applications, it is necessary to classify groups of test
patterns, with patterns in each group generated by the same source.
The co-occurring patterns in a group are statistically dependent due
to the commonality of source. The dependence between these patterns
introduces <i>style context</i> within a group that can be exploited
to improve the classification accuracy. In this paper, we present a
style consistent nearest neighbor classifier that exploits style
context in groups of adjacent patterns to improve the classification
accuracy. We demonstrate the efficacy of the proposed classifier on
a dataset of machine-printed digits where the proposed classifier
reduces the error rate by 64.5%.