DocumentCode
2130264
Title
Kernels for the Investigation of Localized Spatiotemporal Transitions of Drought with Support Vector Machines
Author
Collier, Matthew W. ; McGovern, Amy
Author_Institution
Dept. of Geogr., Univ. of Oklahoma, Norman, OK
fYear
2008
fDate
15-19 Dec. 2008
Firstpage
359
Lastpage
368
Abstract
We present and discuss several spatiotemporal kernels designed to mine real-life and simulated data in support of drought prediction. We implement and empirically validate these kernels for support vector machines. Issues related to the nature of geographic data such as autocorrelation and directionality are investigated.
Keywords
cartography; data mining; geophysics computing; hydrology; rain; support vector machines; data mining; drought prediction; geographic data; localized spatiotemporal transition; support vector machine; Autocorrelation; Conferences; Data mining; Fractals; Kernel; Sampling methods; Space technology; Spatiotemporal phenomena; Support vector machines; Testing; Drought; Geographic Kernels; SVM;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining Workshops, 2008. ICDMW '08. IEEE International Conference on
Conference_Location
Pisa
Print_ISBN
978-0-7695-3503-6
Electronic_ISBN
978-0-7695-3503-6
Type
conf
DOI
10.1109/ICDMW.2008.71
Filename
4733956
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