Farsi handwritten digit recognition based on mixture of RBF experts

IEICE Electronics Express

Soheil Faridi²  Alireza Esmkhani²  Reza Ebrahimpour¹ 

[1] School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM);Brain and Intelligent Systems Research Lab, Department of Electrical Engineering, Shahid Rajaee Teacher Training University

关键词: mixture of experts;    handwritten digit recognition;    loci characterization method;   

DOI  :  10.1587/elex.7.1014

学科分类：电子、光学、磁材料

来源: Denshi Jouhou Tsuushin Gakkai

【 摘 要 】

References(4)Cited-By(5)In this paper, a new classifier combination model is presented for Farsi handwritten digit recognition. The model is consisted of four RBF neural networks as the experts and another RBF network as the gating network which learns to split the input space between the experts. Considering the input data, which is an 81-element vector extracted using the loci characterization method, the gating network assigns a competence coefficient to each expert. The final output is computed as the weighted sum of the outputs of the experts. The recognition rate of the proposed model is 93.5% which is 3.75% more than the rate of the mixture of MLPs experts previously ran on the same database.

【 授权许可】

Unknown