Abstract
The trade-off between the granularity of the data representation
and the recognition accuracy is examined in this paper. We show that
unless a particular criterion is satisfied, there is no guarantee of
achieving a prescribed recognition accuracy. This criterion of interest,
is the mutual information content between the measurements and the class
identities. A novel method for the objective evaluation of intrinsic
error in recognition as sampling rate varies, is described. This
approach is general enough to permit the evaluation of error even when
the parameter under study takes a different form. To demonstrate this we
present results in feature subset selection and multiple classifier
combination. In the case of feature selection, the measurements are the
features. In the case of multiple classifier combination it is the
“quality” of the individual classifiers, evalulated based on
the mutual information between the classifier parameters and class
identities
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