摘要
支持向量机是基于统计学习理论的新一代机器学习技术,其非线性回归预测性能优越于传统统计方法。提出了一种大气污染物浓度预测模型,该方法将支持向量机应用于大气污染物浓度预测,首先对各类影响因子进行分析并进行建模预测;而后利用主成分分析的方法对输入因子降维,从而形成支持向量机的训练样本集;在此基础上建立了基于RBF核函数支持向量回归法的大气污染预模型。大气污染预测实例表明,该方法具有泛化能力强、预测精度高、训练速度快、稳定性好、便于建模等优点,有良好的应用前景。
The support vector machine （SVM） as a new generation machinery learning technology based on statistical theory, has been reported to have better prediction performance of non - liner regression than traditional statistical methods. First, the input variables are analyzed, then dimensionaiity of input variables are reducted using principal component analysis（PCA） to form the training sample of the support vector machine. The appropriate forecasting methods are chosen and an SVM regression model for atmospheric pollution prediction is established. The testing results showed that the model based on support vector machine exhibited its properties of high forecast accuracy, fast training, high generalization capability and easy modeling.
出处
《计算机技术与发展》
2010年第1期250-252,F0003,共4页
Computer Technology and Development
基金
陕西省教育厅专项科研计划项目(07JK312)
关键词
支持向量机
大气污染预测
核函数
support vector machine （SVM）
atmospheric pollution prediction
kernel function
