Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
An adjusted network information criterion for model selection in statistical neural network models
In this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC) criterion, based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The ANIC improves model...
Saved in:
| Format: | Article |
|---|---|
| Published: |
2016
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
Similar Items: An adjusted network information criterion for model selection in statistical neural network models
- Small-Sample Corrected Akaike Information Criterion: An appropriate statistical tool for ranking of adsorption isotherm models
- A model for conflicts’ prediction using deep neural network
- Statistical and neural network approach for estimating monthly evapotranspiration at the international institute of tropical agriculture, Ibadan, Nigeria - a comparative study
- Modelling trends in contraception usage in Nigeria and Ghana
- Influence of social networking on the psychological adjustment of adolescents with hearing impairment in Ibadan, Oyo State, Nigeria
- ARIMA model and neural network: a comparative study of crime rate modelling