Models for Calculating Confidence Intervals for Neural Networks: A Study - Softcover

Inhaltsangabe

This books provides the methodology of analyzing
existing models to calculate confidence intervals on
the results of neural networks. The three techniques
for determining confidence intervals determination
were the non-linear regression, the bootstrapping
estimation, and the maximum likelihood estimation.
The neural network used the backpropagation algorithm
with an input layer, one hidden layer and an output
layer with one unit. The hidden layer had a logistic
or binary sigmoidal activation function and the
output layer had a linear activation function. These
techniques were tested on various data sets with and
without additional noise. The ranges and standard
deviations of the coverage probabilities over 15
simulations for the three techniques were computed.

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