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Advances in Computational Mathematics

, Volume 5, Issue 1, pp 269–280 | Cite as

Linear unlearning for cross-validation

  • Lars Kai Hansen
  • Jan Larsen
Article

Abstract

The leave-one-out cross-validation scheme for generalization assessment of neural network models is computationally expensive due to replicated training sessions. In this paper we suggest linear unlearning of examples as an approach to approximative cross-validation. Further, we discuss the possibility of exploiting the ensemble of networks offered by leave-one-out for performing ensemble predictions. We show that the generalization performance of the equally weighted ensemble predictor is identical to that of the network trained on the whole training set.

Numerical experiments on the sunspot time series prediction benchmark demonstrate the potential of the linear unlearning technique.

Keywords

Neural Network Time Series Numerical Experiment Network Model Training Session 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© J.C. Baltzer AG, Science Publishers 1996

Authors and Affiliations

  • Lars Kai Hansen
    • 1
  • Jan Larsen
    • 1
  1. 1.connect, Electronics Institute B349Technical University of DenmarkLyngbyDenmark

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