4
$\begingroup$

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?

$\endgroup$

1 Answer 1

0
$\begingroup$

Cross-validation can be used to tune any hyperparameter.

One way tune Extreme learning machines (ELM) with cross-validation is to:

  1. Fit a model to a subset of the training dataset. Measure performance on a validation dataset with a evaluation metrics.
  2. Change the number of neurons hyperparameter.
  3. Fit the updated model to a subset of the training dataset. Measure performance on a validation dataset with a evaluation metrics.
  4. Compare the two models and see which model is better on a evaluation metric on their respective validation dataset.
  5. Repeat steps 3-4 until satisfied.
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.