I know that cross validation is used to find the best hyperparameters that minimize the average error. For example, the number of neurons that minimize the average error of cross-validation is estimated in ELM.
But I would like to know how I can apply cross-validation (eg K-Fold) in EM-ELM networks:
Feng, G., Huang, G. B., Lin, Q., & Gay, R. (2009). Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Transactions on Neural Networks, 20 (8), 1352-1357.
Where the architecture is estimated automatically using a training set. What is the best and most standard way to apply cross-validation in EM-ELM? and is it possible to incorporate the cross-validation process into the growth of the network?