Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
1992 | NIPS | bootstrap algorithm,improving neural network predictions,neural network |
Field | DocType | ISBN |
Convergence (routing),Computer science,Bootstrap aggregating,Artificial intelligence,Artificial neural network,Confidence interval,Bootstrapping (electronics),Mathematical optimization,Algorithm,Nonparametric statistics,Analytic solution,Machine learning,Estimator | Conference | 1-55860-274-7 |
Citations | PageRank | References |
11 | 1.60 | 2 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gerhard Paass | 1 | 1136 | 83.63 |