Title :
An incremental unsupervised learning scheme for function approximation
Author :
Bohn, Christian-A
Author_Institution :
Nat. Res. Center for Inf. Technol., St. Augustin, Germany
Abstract :
A new algorithm for general robust function approximation by an artificial neural network is presented. The basis for this work is Fritzke´s supervised growing cell structures approach (1993) which combines supervised and unsupervised learning. It is extended by the capability of resampling the function under examination automatically, and by the definition of a new error measure which enables an accurate approximation of arbitrary goal functions
Keywords :
function approximation; neural nets; unsupervised learning; artificial neural network; error measure; incremental unsupervised learning scheme; robust function approximation; supervised growing cell structures approach; supervised learning; Artificial neural networks; Clustering algorithms; Function approximation; Information technology; Robustness; Testing; Unsupervised learning;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.614169
