DocumentCode
1810540
Title
Weighted least square ensemble networks
Author
Chan, Lai-Wan
Author_Institution
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong
Volume
2
fYear
1999
fDate
36342
Firstpage
1393
Abstract
Ensemble of networks has been proven to give better prediction result than a single network. Two commonly used methods of determining the ensemble weights are simple average ensemble method and the generalized ensemble method. In the paper, we propose a weighted least square ensemble network. The major difference between this method and the other ensemble methods is that we do not assume that neither individual training data nor networks in the ensemble are independent and uncorrelated. Two variances of this model are also introduced, which require fewer computations. The sunspot data was used as a benchmark test of the proposed methods. From the result, we find that for the correlation ensemble, one variance of the weighted least square method gave the best ensemble weightings
Keywords
learning (artificial intelligence); least squares approximations; neural nets; correlation ensemble; ensemble weights; least square ensemble networks; sunspot data; weighted least squares; Benchmark testing; Computer science; Equations; Least squares methods; Neural networks; Training data; Vectors;
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.1999.831167
Filename
831167
Link To Document