A Geographic Weighted Regression Method Based on Semi-supervised Learning

A Geographic Weighted Regression Method Based on Semi-supervised Learning

Geographically weighted regression (GWR) approach will be affected by the quantity of label data. However, it is difficult to get labeled data but easy to get the unlabeled data in applications. Therefore it is indispensable to find an useful way that can use the unlabeled data to improve the regres...

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Bibliographic Details
Main Authors: ZHAO Yangyang, LIU Jiping, XU Shenghua, ZHANG Fuhao, YANG Yi
Format: Article
Language:zho
Published: Surveying and Mapping Press 2017-01-01
Series:Acta Geodaetica et Cartographica Sinica
Subjects:
geographically weighted regression
semi-supervised learning
SSLGWR
population distribution
Online Access:http://html.rhhz.net/CHXB/html/2017-1-123.htm

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