The leave-one-out cross-validation scheme for generalization assessment of neural network models is computationally expensive due to replicated training sessions. Linear unlearning of examples has recently been suggested as an approach to approximative cross-validation. Here we briefly review the linear unlearning scheme, dubbed LULOO, and we illustrate it on a systemidentification example. Further, we address the possibility of extracting confidence information (error bars) from the LULOO ensemble.
|Title of host publication||Proceedings of International Conference on Neural Information Processing|
|Publication status||Published - 1996|
|Event||International Conference on Neural Information Processing - Hong Kong|
Duration: 1 Jan 1996 → …
|Conference||International Conference on Neural Information Processing|
|Period||01/01/1996 → …|