Estimating the Number of Hidden Neurons in a Feedforward Network Using the Singular Value Decomposition

@article{Teoh2006EstimatingTN,
  title={Estimating the Number of Hidden Neurons in a Feedforward Network Using the Singular Value Decomposition},
  author={Eu Jin Teoh and Cheng Xiang and Kay Chen Tan},
  journal={IEEE Transactions on Neural Networks},
  year={2006},
  volume={17},
  pages={1623-1629}
}
In this letter, we attempt to quantify the significance of increasing the number of neurons in the hidden layer of a feedforward neural network architecture using the singular value decomposition (SVD). Through this, we extend some well-known properties of the SVD in evaluating the generalizability of single hidden layer feedforward networks (SLFNs) with respect to the number of hidden layer neurons. The generalization capability of the SLFN is measured by the degree of linear independency of… CONTINUE READING

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