# computing AIC or BIC for nonlinear regression models

Is it possible to calculate AIC or BIC for nonlinear regression models like SVM, regression trees, artificial neural network, and others. AIC and BIC can be estimated from linear models, but I have not seen AIC and BIC being computed for these nonlinear regression models. So, wondering if anyone can provide their opinion with some examples? Thanks.

But why would you do this when packages like rpart automatically cross-validate for you? Cross-validation is almost always better than AIC for model selection. Other models could probably be shoehorned into a framework that makes AIC computation feasible. But the question will generally be: why?