Skip to Main Content
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++
330
Chapter 18
the Kohonen network, not presented in detail here, learn in ways that
exhibit a striking resemblance to creature learning.
Input Normalization
One of the most serious impediments to widespread use of the
Kohonen network is the fact that its inputs are subject to serious
restrictions. Ideally, they should he within symmetric boimds, usually
taken to be [-1, 1]. The length of the input vector must be the same
for all training and test cases. This length is typically chosen to be 1.
Finally, for best performance, each input should be able to cover the
majority of its range. For example, if some of the input variables are
physicall ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Neural Networks with Keras Cookbook

Neural Networks with Keras Cookbook

V Kishore Ayyadevara
Scientific Computing with Python 3

Scientific Computing with Python 3

Claus Führer, Claus Fuhrer, Jan Erik Solem, Olivier Verdier
Mastering Java Machine Learning

Mastering Java Machine Learning

Uday Kamath, Krishna Choppella

Publisher Resources

ISBN: 9780080514338