On Hidden Nodes for Neural Nets
G. Mirchandani and W. Cao
IEEE Transactions on Circuits and Systems - Special Issue on
Neural Networks, Vol. 36, No.5, pp.661-664, May, 1989
(Paper)


Recent results indicate that the number of hidden nodes (H) in a feedforward neural net depend only on the number of input training patterns (T). There appear to be conjectures that H is of the order of (T-1) and log_2 T. The main contribution of this work is a proof that maximum number of separable regions (M) in the input space is a function of both the H and the input space dimension (d). We also show that H = M - 1 and H = log_2 M are special cases of that formulation. M defines a lower bound on T, the number of input patterns that may be used for training. Application to some experiments are investigated.

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