| Literature DB >> 18282870 |
K G Mehrotra1, C K Mohan, S Ranka.
Abstract
The relationship between the number of hidden nodes in a neural network, the complexity of a multiclass discrimination problem, and the number of samples needed for effect learning are discussed. Bounds for the number of samples needed for effect learning are given. It is shown that Omega(min (d,n) M) boundary samples are required for successful classification of M clusters of samples using a two-hidden-layer neural network with d-dimensional inputs and n nodes in the first hidden layer.Year: 1991 PMID： 18282870 DOI： 10.1109/72.97932
Source DB: PubMed Journal: IEEE Trans Neural Netw ISSN： 1045-9227