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Prototype-based minimum error classifier for handwritten digits recognition

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2 Author(s)
Nopsuwanchai, R. ; Comput. Lab., Cambridge Univ., UK ; Biem, A.

The paper describes an application of the prototype-based minimum error classifier (PBMEC) to the offline recognition of handwritten digits. The PBMEC uses a set of prototypes to represent each digit along with an Lν-norm of distances as the decoding scheme. Optimization of the system is based on the minimum classification error (MCE) criterion. We introduce a new clustering criterion adapted to the PBMEC structure that minimizes an Lν-norm-based distortion measure. The new clustering algorithm can generate a smaller number of prototypes than the standard k-means with no loss in accuracy. It is also shown that the PBMEC trained with MCE can achieve over 42% improvement from the baseline k-means process and requires only 28 Kb storage to match the performance of a 1.46 Mb sized k-NN classifier.

Published in:

Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on  (Volume:5 )

Date of Conference:

17-21 May 2004

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