Mirror Image Learning for Autoassociative Neural Networks
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A discriminated correlation classifier for face recognition
Proceedings of the 2010 ACM Symposium on Applied Computing
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Abstract: This paper proposes a new corrective learning algorithm and evaluates the performance by handwritten numeral recognition test. The algorithm generates a mirror image of a pattern that belongs to one class of a pair of confusing classes and utilizes it as a learning pattern of the other class. This paper also studies on how to extract confusing patterns within a certain margin of a decision boundary to generate enough number of mirror images, and how to perform an effective mirror image compensation to in-crease the margin. Recognition accuracies of the minimum distance classifier and the projection distance method were improved from 93.17% to 98.38% and from 99.11% to 99.41% respectively in the recognition test for handwritten numeral database IPTP CD-ROM1.