Linear Unlearning for Cross-Validation

Lars Kai Hansen, Jan Larsen

    Research output: Contribution to journalJournal articleResearchpeer-review

    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
    Original languageEnglish
    JournalAdvances in Computational Mathematics
    Volume5
    Issue number2-3
    Pages (from-to)269-280
    ISSN1019-7168
    Publication statusPublished - 1996

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