IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Computers and Operations Research
A hybrid neural network model based reinforcement learning agent
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Generalized single-hidden layer feedforward networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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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 the patterns in hidden layer space, which can be indirectly quantified from the singular values obtained from the SVD, in a postlearning step. A pruning/growing technique based on these singular values is then used to estimate the necessary number of neurons in the hidden layer. More importantly, we describe in detail properties of the SVD in determining the structure of a neural network particularly with respect to the robustness of the selected model