DocumentCode
3508827
Title
A comparative study of output representation schemes for multilayer neural networks
Author
Lu, Bao Liang ; Ito, Koji
Author_Institution
Bio-Mimetic Control Res. Center, RIKEN, Atsuta, Japan
fYear
1995
fDate
26-28 Jul 1995
Firstpage
1535
Lastpage
1538
Abstract
In this paper, we compare the 1-out-of-N representation scheme with three distributed ones, namely binary, Gray, and simple-sum. We put the emphasis on the training time, learning accuracy, and generalization capability. In order to evaluate the performance of these schemes, three multilayer neural networks (multilayer perceptron, multilayer quadratic perceptron, and multi-sieving network) are used to learn the vowel recognition and image segmentation problems
Keywords
feedforward neural nets; generalisation (artificial intelligence); image segmentation; learning (artificial intelligence); multilayer perceptrons; performance evaluation; speech recognition; generalization; image segmentation; learning time; multi-sieving network; multilayer neural networks; multilayer perceptron; output representation; vowel recognition; Binary codes; Creep; Multi-layer neural network; Multilayer perceptrons; Neural networks; Nonhomogeneous media; Reflective binary codes; Samarium; Supervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
SICE '95. Proceedings of the 34th SICE Annual Conference. International Session Papers
Conference_Location
Hokkaido
Print_ISBN
0-7803-2781-0
Type
conf
DOI
10.1109/SICE.1995.526962
Filename
526962
Link To Document