|
Abstract : |
Abstract: Transformation invariance is known to be fundamental for excellent performances in pattern recognition. One of the most successful approach is tangent distance, originally proposed for a nearest-neighbor algorithm (Simard, LeCun and Denker, 1993). The resulting classifier, however, has a very high computational complexity and, perhaps more important, lacks discrimination capabilities. We present a discriminant learning algorithm for a modular classifier based on several autoassociative neural networks. Tangent distance as objective function guarantees efficient incorporation of transformation invariance. The system achieved a raw error rate of 2.6 % and a rejection rate of 3.6 % on the NIST uppercase letters. 1, |
