Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm

Part of Advances in Neural Information Processing Systems 5 (NIPS 1992)

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Gerhard Paass


The bootstrap algorithm is a computational intensive procedure to derive nonparametric confidence intervals of statistical estimators in situations where an analytic solution is intractable. It is ap(cid:173) plied to neural networks to estimate the predictive distribution for unseen inputs. The consistency of different bootstrap procedures and their convergence speed is discussed. A small scale simulation experiment shows the applicability of the bootstrap to practical problems and its potential use.