摘要：This paper considers the feed-forward neural network models for data
of mutually exclusive groups and a set of predictor variables. We take
into account the bootstrapping based on information criterion when selecting
the optimum number of hidden units for a neural network model
and the deviance in order to summarize the measure of goodness-of-fit on
fitted neural network models. The bootstrapping is also adapted in order
to provide estimates of the bias of the excess error in a prediction rule
constructed with training samples. Simulated data from known (true)
models are analyzed in order to interpret the results using the neural
network. In addition, the thyroid disease database, which compares estimated
measures of predictive performance, is examined in both a pure
training sample study and in a test sample study, in which the realized
test sample apparent error rates associated with a constructed prediction
rule are reported. Apartment house data of the metropolitan area station
with four-class classification are also analyzed in order to assess the bootstrapping
by comparing leaving-one-out cross-validation (CV).
