On a multiple nodes fault tolerant training for RBF : Objective function , sensitivity analysis and relation to generalization

@inproceedings{Sum2005OnAM,
  title={On a multiple nodes fault tolerant training for RBF : Objective function , sensitivity analysis and relation to generalization},
  author={John Sum},
  year={2005}
}
Over a decades, although various techniques have been proposed to improve the training of a neural network to against node fault, there is still a lacking of (i) a simple objective function to formalize multiple nodes fault and not much work has been done on understanding of the relation between fault tolerant and generalization. In this paper, an objective function based on the idea of Kullback-Leibler divergence is presented for multiple nodes fault tolerant training. It is essentially the… CONTINUE READING

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References

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Showing 1-10 of 20 references

A measure of fault tolerance for functional

O Fontenla-Romero
networks, Neurocomputing, • 2004
View 1 Excerpt

et al , A measure of fault tolerance for functional networks

O. Fontenla-Romero
Neurocomputing • 2004

Synthesis of fault tolerance neural networks

S. PhatakD., E. Tcherner
2002

Tcherner, Synthesis of fault tolerance neural networks

D.S.E. Phatak
Proc. IJCNN’02, • 2002
View 1 Excerpt