DocumentCode
314394
Title
The effects of quantization on the backpropagation learning
Author
Ikeda, Kazushi ; Suzuki, Akihiro ; Nakayama, Kenji
Author_Institution
Dept. of Electr. & Comput. Eng., Kanazawa Univ., Japan
Volume
3
fYear
1997
fDate
9-12 Jun 1997
Firstpage
1896
Abstract
The effects of the quantization of the parameters of a learning machine are discussed. The learning coefficient should be as small as possible for a better estimate of parameters. On the other hand, when the parameters are quantized, it should be relatively larger in order to avoid the paralysis of learning originated from the quantization. How to choose the learning coefficient is given in this paper from the statistical point of view
Keywords
backpropagation; neural nets; quantisation (signal); statistical analysis; backpropagation learning; learning coefficient; parameter estimation; quantization; statistical approach; Backpropagation algorithms; Circuits; Computer errors; Equations; Machine learning; Multilayer perceptrons; Neurons; Parameter estimation; Quantization; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks,1997., International Conference on
Conference_Location
Houston, TX
Print_ISBN
0-7803-4122-8
Type
conf
DOI
10.1109/ICNN.1997.614188
Filename
614188
Link To Document