DocumentCode
2615877
Title
Unsupervised learning for neural trees
Author
Fang, Luycan ; Jennings, Andrew ; Wen, Wilson X. ; Li, Ken Q Q ; Li, T.
Author_Institution
Telecom Australia Res. Labs., Clayton, Vic., Australia
fYear
1991
fDate
18-21 Nov 1991
Firstpage
2709
Abstract
A self-organizing neural tree is studied. The neural tree is suited to hierarchical classifications. Unsupervised learning algorithms have been developed for the neural tree. A simulation study indicated that the vectors represented by the nodes of the tree tend to approximate the probability of the sample distribution. The neural tree has been applied to speech recognition and image coding. Promising results have been obtained
Keywords
learning systems; neural nets; picture processing; probability; speech recognition; trees (mathematics); hierarchical classifications; image coding; learning systems; neural nets; probability; sample distribution; self-organizing neural tree; speech recognition; unsupervised learning; vectors; Artificial intelligence; Classification algorithms; Classification tree analysis; Computer science; Image coding; Network topology; Neural networks; Telecommunications; Tree data structures; Unsupervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN
0-7803-0227-3
Type
conf
DOI
10.1109/IJCNN.1991.170278
Filename
170278
Link To Document