Class-specific feature sets in classification

In this correspondence, we present a new approach to the design of probabilistic classifiers that circumvents the dimensionality problem. Rather than working with a common high-dimensional feature set, the classifier is written in terms of likelihood ratios with respect to a common class using sufficient statistics chosen specifically for each class.

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