DocumentCode
2389768
Title
A method for training a feed-forward neural net model while targeting reduced nonlinearity
Author
Koutsougeras, Cris ; Papadourakis, George
Author_Institution
Dept. of Comput. Sci., Tulane Univ., New Orleans, LA, USA
fYear
1991
fDate
10-13 Nov 1991
Firstpage
192
Lastpage
199
Abstract
In the analysis presented for feedforward neural networks, the causes of problems in the adaptation of current models are examined. A new method for training a feedforward neural net model is introduced. The method encompasses elements of both supervised and unsupervised learning. The development of internal representations is no more an issue tangential to the curve fitting objectives of the other known supervised learning methods. Curve fitting remains as a primary objective but unsupervised learning techniques are also used in order to aid the development of internal representations. The net structure is incrementally formed, thus allowing the formation of a structure of reduced nonlinearity
Keywords
learning systems; neural nets; curve fitting; feed-forward neural net model; reduced nonlinearity; supervised learning; training; unsupervised learning; Computer science; Curve fitting; Feedforward neural networks; Feedforward systems; Feeds; Learning systems; Neural networks; Robustness; Sampling methods; Unsupervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Tools for Artificial Intelligence, 1991. TAI '91., Third International Conference on
Conference_Location
San Jose, CA
Print_ISBN
0-8186-2300-4
Type
conf
DOI
10.1109/TAI.1991.167095
Filename
167095
Link To Document