Estimation of Mutual Information in Two-Class Pattern Recognition
IEEE Transactions on Computers
Feature Evalution with Measures of Probabilistic Dependence
IEEE Transactions on Computers
On dependence and discrimination in pattern recognition
IEEE Transactions on Computers
A GA-based feature selection approach with an application to handwritten character recognition
Pattern Recognition Letters
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A simple yet powerful technique for selecting features to be used in a pattern recognition system has been devised and applied to an eight-class character recognition problem using a set of 19 000 typed character samples as a data base. High-order joint probabilities have been directly estimated from the data base, thus making it possible to take into account in the feature selection process the existence of statistical dependencies among features to a greater extent than has been done in previously reported work.