Climate model by SVM based on experienced knowledge in tobacco region division

Tobacco region division is vital to improve the quality of the tobacco. And the climate model is the most important factor for the division. However, the climate variable, which was strongly corrupted by noises or fluctuations, can not be reconstructed by common method. In order to improve the perfo...

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Hauptverfasser:	Wang Deji, Xu Bo, Li Guangcai, Chen Guoqun, Sui Bingyu
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Biological system modeling Cities and towns Climate Model Experienced Knowledge Kernel Meteorology Monopoly Neural networks Pipelines Research and development Support vector machine classification Support vector machines SVM Tobacco Region Division
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Beschreibung
Zusammenfassung:	Tobacco region division is vital to improve the quality of the tobacco. And the climate model is the most important factor for the division. However, the climate variable, which was strongly corrupted by noises or fluctuations, can not be reconstructed by common method. In order to improve the performance of regression, the experienced knowledge about climate variable is incorporated in the training of SVM. The experimental results demonstrate the effectiveness and efficiency of our approach.
ISSN:	1948-9439 1948-9447
DOI:	10.1109/CCDC.2009.5192581