| Paper: | MLSP-P7.9 | ||
| Session: | Pattern Recognition and Classification II | ||
| Time: | Friday, May 21, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification | ||
| Title: | PROTOTYPE-BASED MINIMUM ERROR CLASSIFIER FOR HANDWRITTEN DIGITS RECOGNITION | ||
| Authors: | Roongroj Nopsuwanchai; University of Cambridge | ||
| Alain Biem; IBM T. J. Watson Research Center | |||
| Abstract: | This paper describes an application of the prototype-based minimum classification error classifier (PBMEC) to the offline recognition of handwritten digits. The PBMEC uses a set of prototypes to represent each digit along with an Lp-norm of distances as the decoding scheme. Optimization of the system is based on the Minimum Classification Error criterion (MCE). In this paper, we introduce a new clustering criterion adapted to the PBMEC structure that minimizes an Lp 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 the MCE realizes more than a 42% improvement from the baseline k-means process and requires only 28Kb storage to match the performance of a 1.46MB-sized k-NN classifier. | ||
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