DocumentCode
291909
Title
Similarities of LVQ and RBF learning-a survey of learning rules and the application to the classification of signals from high-resolution electrocardiography
Author
Schwenker, F. ; Kestler, H.A. ; Palm, G. ; Höher, M.
Author_Institution
Dept. of Neural Inf. Processing, Ulm Univ., Germany
Volume
1
fYear
1994
fDate
2-5 Oct 1994
Firstpage
646
Abstract
In this paper algorithms for neural network training are described. We discuss the apparent similarities of LVQ and RBF classification which motivate us to combine the two approaches. The resulting algorithm is then tested on features extracted from signals from high-resolution electrocardiography
Keywords
electrocardiography; feedforward neural nets; learning (artificial intelligence); pattern classification; vector quantisation; LVQ learning; RBF learning; high-resolution electrocardiography; linear vector quantisation; radial basis functions; signal classification; Artificial neural networks; Biomedical optical imaging; Cardiography; Cognition; Feedforward neural networks; Neural networks; Neurons; Phase estimation; Prototypes; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on
Conference_Location
San Antonio, TX
Print_ISBN
0-7803-2129-4
Type
conf
DOI
10.1109/ICSMC.1994.399913
Filename
399913
Link To Document