The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
Support vector regression with ANOVA decomposition kernels
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
An overview of statistical learning theory
IEEE Transactions on Neural Networks
International Journal of Remote Sensing - Satellite observations of the atmosphere, ocean and their interface in relation to climate, natural hazards and management of the coastal zone
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A major task of ocean colour observations is to determine the distribution of phytoplankton primary production. At present, the global coverage of the sea surface chlorophyll concentration, sea surface temperature, photosynthetically available radiation (PAR) can nominally be achieved every 1 to 2 days with standard algorithms from satellite data. From these standard products, a variety of bio-optical algorithms has been developed to estimate ocean primary productivity. In this communication, we have investigated the possibility of using a novel universal approximator-support vector machine (SVM) as the nonlinear transfer function between ocean primary productivity and the information that can be retrieved from satellite data, including chlorophyll concentration, PAR, maximum carbon fixation rate and day length, which is the same as the vertically generalized production model (VPGM). The VGPM dataset was used to evaluate the proposed approach. The primary production algorithm round robin 2 (PPARR2) dataset was used to further compare the precision between the VGPM and the SVM model. The results suggest that the SVM model is more accurate than the VGPM. Using the SVM model to calculate the global ocean primary productivity, the result is 45.5 Pg C yr-1, which is a little higher than the VGPM result.