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This study proposed a novel HPSO-SVR model that hybridized the particle swarm optimization (PSO) and support vector regression (SVR) to improve the regression accuracy based on the type of kernel function and kernel parameter value optimization with a small and appropriate feature subset, which is then applied to forecast the monthly rainfall. This optimization mechanism combined the discrete PSO...
This paper presents a new method for hyperspectral image classification. It combines support vector machine (SVM), particle swarm optimization (PSO), and genetic algorithm (GA) together. Its aim is to improve the classification accuracy and reduce the computation consumption based on heuristic algorithms. Because the classification accuracy is impacted by the parameters of the SVM model and feature...
A novel support vector regression (SVR) optimized by an integrated particle swarm optimization (PSO) was proposed. The optimization mechanism combined the discrete-valued PSO with the continuous-valued PSO to optimize the input feature subset selection and the SVR kernel parameter setting. By incorporating two types of PSO, the parameters and the input features of SVR were optimized simultaneously...
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