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“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 11615
School of Cognitive Sciences
  Title:   Farsi handwritten digit recognition based on mixture of RBF experts
  Author(s): 
1.  Reza Ebrahimpour
2.  Alireza Esmkhani
3.  Soheil Faridi
  Status:   Published
  Journal: IEICE Electronics Express
  No.:  14
  Vol.:  7
  Year:  2010
  Pages:   1014-1019
  Supported by:  IPM
  Abstract:
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

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