Prototype-based minimum error classifier for handwritten digits recognition

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 L_ν-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 L_ν-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.