By Topic

Optimisation of digital learning networks when applied to pattern recognition of mass spectra

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $33
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
T. J. Stonham ; University of Kent at Canterbury, Electronics Laboratories, Canterbury, UK ; I. Aleksander

A pattern classifier employing n-tuple sampling digital learning networks is analysed to show that redundancy can occur both due to the common occurrence of sets of n-tuples of the sample pattern and invariant points in the patterns. Some experimental results are given for a mass-spectrum classifier, where the system has been optimised by reconnection to reduce this redundancy.

Published in:

Electronics Letters  (Volume:10 ,  Issue: 15 )