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- W43103345 abstract "Transformation invariance is known to be fundamental for excellent performances in pat- tern recognition. One of the most successful approach is tangent distance, originally propos ed for a nearest-neighbor algorithm (Simard, LeCun and Denker, 1993). The resulting classifier, how ever, has a very high computational complexity and, perhaps more important, lacks discrimination ca- pabilities. We present a discriminant learning algorithm for a modular classifier based on several autoassociative neural networks. Tangent distance as objective function guarantees efficie nt incor- poration 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." @default.
- W43103345 created "2016-06-24" @default.
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- W43103345 date "1995-01-01" @default.
- W43103345 modified "2023-09-26" @default.
- W43103345 title "Learning Discriminant Tangent Models for Handwritten Character Recognition" @default.
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