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Practical Neural Network Recipies in C++
book

Practical Neural Network Recipies in C++

by Masters
June 2014
Intermediate to advanced content levelIntermediate to advanced
493 pages
20h 30m
English
Morgan Kaufmann
Content preview from Practical Neural Network Recipies in C++
Probabilistic
Neural Networks
211
Finally, it should
be
pointed
out
that [Kim
and
Arozullah,
1992]
have proposed a sweeping generalization of the entire probabilistic
neural network paradigm. They estimate the underlying probability
density functions based on the Gram-Charlier series expansion, with
optional use of Parzen windows. Significantly improved performance
is claimed.
A Sample Program
This section offers a subroutine for classifying using the probabilistic
neural network (Parzen-Bayes classifier). A subroutine for automati-
cally choosing an optimal value for the scaling parameter σ is also
given.
int pnn (
int nvars , // Number of
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Publisher Resources

ISBN: 9780080514338