DocumentCode
1842413
Title
Statistical method of pruning neural networks
Author
Lo, James T.
Author_Institution
Dept. of Math. & Stat., Maryland Univ., Baltimore, MD, USA
Volume
3
fYear
1999
fDate
1999
Firstpage
1678
Abstract
A statistical method of pruning multilayer perceptrons is proposed which is expected to involve less computation than does “optimal brain surgeon”. The statistical method iteratively performs the steps of estimating the error covariance of the weights, evaluating the z-statistics for the weights, pruning the weights selected by hypothesis testing, and re-training the neural network. An interesting relationship between the statistical method and “optimal brain surgeon” is discussed. The relationship provides some insight into these two methods
Keywords
covariance matrices; iterative methods; learning (artificial intelligence); multilayer perceptrons; statistical analysis; covariance matrix; iterative method; learning; multilayer perceptrons; neural networks; pruning; statistical method; Artificial neural networks; Biological neural networks; Error analysis; Mathematics; Neural networks; Statistical analysis; Surges; Testing; Training data; Yttrium;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.832626
Filename
832626
Link To Document