Multi-SOMs for classification
by Nils Goerke, Florian Kintzler, Bernd Bruggemann
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 2, No. 2/3, 2007

Abstract: We propose a method to use the classification capabilities of self organising neural networks to extract symbolic information from raw data. The Multi-SOM (M-SOM) approach is a variant of Self Organising Maps (SOM). Multi-SOMS consist of a set of partner SOMs, trained simultaneously and in concurrence to each other, to adapt to different classes. The trained M-SOM transforms the non-linear time series of a strange attractor into a stream of symbols, adequate for further classification or for control tasks. We are convinced, that using the Multi-SOM approch for classification, gives a variety of new applications.

Online publication date: Mon, 19-Feb-2007

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Systems Technologies and Applications (IJISTA):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com