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
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;
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
DOI :
10.1109/IJCNN.1991.170278
