A statistical technique using resampling with replacement.
A random sample is selected by sampling with replacement from the data set and is used to train the network. The trained network is then tested on the remaining data. This procedure is repeated a large number of times. The average of all such test errors is an estimate of the generalisation performance metric.
A numerical method - also referred to as Bootstrap simulation - for inferring sampling distributions and confidence intervals for statistics of random variables. The methodology to estimate uncertainty involves generating subsets of the data on the basis of random sampling with replacements as the data are sampled. Such re-sampling means that each datum is equally represented in the randomization scheme (statistics). [7
is construction of artificial data batches using sampling with replacements [pg. 146, 3
(Sampling Method Used in Cladistics)
Training data sets are created by re-sampling with replacement from the original training set, so data records may occur more than once. In other words, this method treats a sample as if it were the entire population. Usually, final estimates are obtained by taking the average of the estimates from each of the bootstrap test sets.
In statistics, bootstrapping is a modern, computer-intensive, general purpose approach to statistical inference, falling within a broader class of resampling methods.