Title :
A structural learning by adding independent noises to hidden units
Author :
Kurita, T. ; Asoh, H. ; Umeyama, S. ; Akaho, S. ; Hosomi, A.
Author_Institution :
Math. Inf. Section, Electrotech. Lab., Ibaraki, Japan
fDate :
27 Jun-2 Jul 1994
Abstract :
The paper demonstrates that a skeletal structure of a network emerges when independent noises are added to the inputs of the hidden units of multilayer perceptron during the learning by error backpropagation. By analyzing the average behavior of the error backpropagation algorithm to such noises, it is shown that the weights from the hidden units to the output units tend to get smaller and the outputs of the hidden units tend to be 0 or 1. Such tendency have been demonstrated by experiments of learning of pattern classification problem
Keywords :
backpropagation; multilayer perceptrons; pattern classification; average behavior; error backpropagation; hidden units; independent noises; multilayer perceptron; pattern classification problem; skeletal structure; structural learning; weights; Algorithm design and analysis; Backpropagation algorithms; Gaussian noise; Informatics; Linearity; Multilayer perceptrons; Noise figure; Noise measurement; Pattern classification;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374174
