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Multiple network fusion using fuzzy logic

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2 Author(s)
Sung-Bae Cho ; Human Inf. Process. Res. Lab., ATR, Kyoto, Japan ; Kim, J.H.

Multiplayer feedforward networks trained by minimizing the mean squared error and by using a one of c teaching function yield network outputs that estimate posterior class probabilities. This provides a sound basis for combining the results from multiple networks to get more accurate classification. This paper presents a method for combining multiple networks based on fuzzy logic, especially the fuzzy integral. This method non-linearly combines objective evidence, in the form of a network output, with subjective evaluation of the importance of the individual neural networks. The experimental results with the recognition problem of on-line handwriting characters show that the performance of individual networks could be improved significantly

Published in:

Neural Networks, IEEE Transactions on  (Volume:6 ,  Issue: 2 )

Date of Publication:

Mar 1995

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