Models for Calculating Confidence Intervals for Neural Networks

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.

Autorentext

Ashutosh Nandeshwar has a master's degree in industrial engineering from West Virginia University and is working on his dissertation. He is working as an institutional research information officer at Kent state university. He is a member of Alpha Pi Mu, the honorary society for industrial engineering, and association of institutional research.

Klappentext
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.