The problem of improving the capability of statistical character classifiers based on finite and sparse training sets is addressed. A significant improvement is obtained coupling standard classifiers based on the k-nearest neighbours technique with a second higher level classification stage. This method has been applied to three existing classifiers reducing the error rate at zero rejection of ~ 17%. © 1993, The Institution of Electrical Engineers. All rights reserved.
Improved handwritten character recognition using second-order information from training set
Kovacs Z. M.;
1993-01-01
Abstract
The problem of improving the capability of statistical character classifiers based on finite and sparse training sets is addressed. A significant improvement is obtained coupling standard classifiers based on the k-nearest neighbours technique with a second higher level classification stage. This method has been applied to three existing classifiers reducing the error rate at zero rejection of ~ 17%. © 1993, The Institution of Electrical Engineers. All rights reserved.File in questo prodotto:
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