DocumentCode :
1797245
Title :
Data Mining Paradigm Based on Functional Networks with Applications in Landslide Prediction
Author :
Ailong Wu ; Zhigang Zeng ; Chaojin Fu
Author_Institution :
Coll. of Math. & Stat., Hubei Normal Univ., Huangshi, China
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
2826
Lastpage :
2830
Abstract :
In this paper, a new intelligence paradigm scheme to forecast landslide based on functional networks is presented. Both methodology and learning algorithm for this kind of intelligence system paradigm using the minimax method are derived. The performance and validity of the new functional networks intelligence paradigm are demonstrated by using real-world example. The results show that the landslide prediction using functional networks is reasonable, effective and achieves a high-quality performance.
Keywords :
data mining; geomorphology; geophysics computing; learning (artificial intelligence); minimax techniques; data mining paradigm; functional network intelligence paradigm; high-quality performance; intelligence system paradigm; landslide forecasting; landslide prediction; learning algorithm; minimax method; Biological neural networks; Data mining; Educational institutions; Geology; Predictive models; Terrain factors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
Type :
conf
DOI :
10.1109/IJCNN.2014.6889362
Filename :
6889362
Link To Document :
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