DocumentCode
303365
Title
A constructive learning algorithm for an HME
Author
Saito, Kazumi ; Nakano, Ryohei
Author_Institution
NTT Commun. Sci. Lab., Kyoto, Japan
Volume
2
fYear
1996
fDate
3-6 Jun 1996
Firstpage
1268
Abstract
A hierarchical mixtures of experts (HME) model has been applied to several classes of problems, and its usefulness has been shown. However, defining an adequate structure in advance is required and the resulting performance depends on the structure. To overcome this problem, a constructive learning algorithm for an HME is proposed; it includes an initialization method, a training method and an extension method. In our experiments, which used parity problems and a function approximation problem, the proposed algorithm worked much better than the conventional method
Keywords
function approximation; learning (artificial intelligence); neural nets; constructive learning algorithm; extension method; function approximation problem; hierarchical mixtures of experts model; initialization method; parity problems; training method; Approximation algorithms; Binary trees; Classification tree analysis; Computer networks; Feedforward systems; Function approximation; Input variables; Laboratories; Telephony;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1996., IEEE International Conference on
Conference_Location
Washington, DC
Print_ISBN
0-7803-3210-5
Type
conf
DOI
10.1109/ICNN.1996.549080
Filename
549080
Link To Document