By Topic

Maximum-likelihood design of layered neural networks

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 $13
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

1 Author(s)
J. Grim ; Inst. of Inf. Theory & Autom., Czechoslovak Acad. of Sci., Prague, Czech Republic

The design of layered neural networks is posed as a problem of estimating finite mixtures of normal densities in the framework of statistical decision-making. The output units of the network (third layer) correspond to class-conditional mixtures defined as weighted sums of a given set of normal densities which can be viewed as radial basis functions. It is shown that the resulting classification performance strongly depends on the component densities (second layer) shared by the class conditional mixtures. To enable a global optimization of layered neural networks the EM algorithm is modified to compute m.-l. estimates of finite mixtures with shared components

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996