Conference paper
Cross validation in LULOO
In
Proceedings of International Conference on Neural Information Processing
—
1996
Abstract
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.
| Language: | English |
|---|---|
| Year: | 1996 |
| Proceedings: | International Conference on Neural Information Processing |
| Types: | Conference paper |
| ORCIDs: | Hansen, Lars Kai and Larsen, Jan |
