...网 ANN）、核主成分回归（KemeIPCR， KPCR）、核偏最小二乘法（KemelPLS，KPLS）和支持向量机回归（Support Vector regression，sVR）等；集成（或共识）的建模策略，如Boosting，Bagging和stacking 等；基于局部样本...
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为了更准确的预测油藏四个区域的物性参数，本文提出了结合粗糙集属性约简和支持向量机回归的方法。
To predict reservoir characteristic parameters of four regions exactly, a method based on the attribute reduction by the rough set and SVR is presented.
介绍了支持向量机分类和回归算法，将其应用于梁结构的损伤诊断中。
This paper introduces the support vector classification and regression algorithms, which are applied to the structure damage identification.
结果表明，支持向量机回归和预测的最大相对误差不超过6 5%。
The results show that the maximum regression and prediction relative errors are not greater than 6.5%.
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