Conference item icon

Conference item

SUPERVISED AND UNSUPERVISED LEARNING IN RADIAL BASIS FUNCTION CLASSIFIERS

Abstract:

The paper considers a number of strategies for training radial basis function (RBF) classifiers. A benchmark problem is constructed using ten-dimensional input patterns which have to be classified into one of three classes. The RBF networks are trained using a two-phase approach (unsupervised clustering for the first layer followed by supervised learning for the second layer), error backpropagation (supervised learning for both layers) and a hybrid approach. It is shown that RBF classifiers t...

Expand abstract
Publication status:
Published

Actions


Access Document


Publisher copy:
10.1049/ip-vis:19941324

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Institute of Biomedical Engineering
Role:
Author
Publisher:
IEE
Volume:
141
Issue:
4
Pages:
210-216
Host title:
IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING
Publication date:
1994-08-01
DOI:
ISSN:
1350-245X
Source identifiers:
61744
Keywords:
Pubs id:
pubs:61744
UUID:
uuid:cfb5bdaa-c6f7-4513-886b-c43f3e2287b3
Local pid:
pubs:61744
Deposit date:
2012-12-19

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP