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 :
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