Title :
Fast recognition of handwritten digits using pairwise coupling support vector machine
Author :
Zeyu, Li ; ShiWei, Tang ; Hao, Wang
Author_Institution :
Nat. Lab. on Machine Perception, Peking Univ., Beijing, China
fDate :
6/24/1905 12:00:00 AM
Abstract :
In this paper, a hierarchical structure combining a linear classifier based on the Mahalanobis distance and pairwise coupling (PWC) is proposed to effectively tackle multi-class classification problem. By taking the advantage of the distribution information of the dataset, both the recognition rate and speed are all improved. At the same time, the proposed method can estimate the posterior probabilities of a testing pattern more accurately than the classical PWC in multi-class cases. Experimental results on handwritten digit recognition demonstrate the effectiveness and efficiency of our method
Keywords :
handwritten character recognition; learning automata; neural nets; optimisation; pattern classification; probability; Mahalanobis distance; handwritten digit recognition; hierarchical structure; linear classifier; multiple class pattern classification; optimization; pairwise coupling; posterior probability; support vector machine; Computer science; Educational institutions; Face recognition; Handwriting recognition; Laboratories; Support vector machine classification; Support vector machines; Testing; Text recognition; Voting;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005590