Citation

Wang W, Liu Y, Li GZ, Wu GF, Ma QZ, Zhao LF, Lin MZ. Acta Seismol. Sin. Engl. Ed. 2006; 19(1): 30-38.

Affiliation

Earthquake Administration of Shanghai Municipality, Shanghai 200062, China

Copyright

(Copyright © 2006, Seismological Society of China, Publisher Holtzbrinck Springer Nature Publishing Group)

DOI

unavailable

PMID

unavailable

Abstract

Statistical learning theory is for small-sample statistics. And support vector machine is a new machine learning method based on the statistical learning theory. The support vector machine not only has solved certain problems in many learning methods, such as small sample, over fitting, high dimension and local minimum, but also has a higher generalization (forecasting) ability than that of artificial neural networks. The strong earthquakes in Chinese mainland are related to a certain extent to the intensive seismicity along the main plate boundaries in the world, however, the relation is nonlinear. In the paper, we have studied this unclear relation by the support vector machine method for the purpose of forecasting strong earthquakes in Chinese mainland.