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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...
The performance and regression precision of weak learners (accuracies should be greater than 0.5) for pattern recognition and forecasting can be upgraded using AdaBoost algorithm. Support vector machine (SVM) is a state-of-the-art learning machines and have been widely used in pattern recognition area since 90's of 20th contrary, however the performance of SVM is not stable and easily influenced due...
The forecast of air passenger flow plays an important role in the management of airline, but the traditional forecast methods can't guarantee the generalization capability when they face a large-scale, multi-dimension, nonlinear and non-normal distribution time series data. To improve the forecast ability of air passenger flow, the SVM regression algorithm is introduced in this paper. By selecting...
Sewage treatment system is a complicated nonlinear system with multi-variables, chemical reaction, biological process and altered loads, hard to describe mathematically. Thus prediction of the effluent quality of sewage treatment plant through a mathematical model has being a challenge. In this paper we adopts regression support vector machine (SVM) to set up a prediction model of a sewage treatment...
This paper presents a distributed support vector regression (SV R) algorithm for sensor networks. The idea behind this algorithm is to make use of the structure similarity between sensor networks and SV Rs with 2D input data in order to implement SV R in a distributed way. During training stage, each sensor node provides its 2D coordinates as an input pattern and a sensory data as an output to the...
The choice of kernels is important for the support vector regression (SVR). In this paper, the robustness of SVR with different kernels is empirically analyzed. The two typical kernels, polynomial kernel and radial basis function (RBF) kernel, and their hybrid are used. Two simple rules for composition of kernels are used to produce the hybrid kernels. The experimental results show that the SVR with...
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