Cross-Validation with LULOO
نویسندگان
چکیده
| The leave-one-out cross-validation scheme for generalization assessment of neu-ral 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 brieey review the linear unlearning scheme, dubbed LULOO, and we illustrate it on a system identiication example. Further, we address the possibility of extracting conndence information (error bars) from the LULOO ensemble.
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