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
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;
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
DOI :
10.1109/ICDMW.2008.71
