计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (3): 42-44.
• 学术探讨 • 上一篇 下一篇
王 芳,杨慧中
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WANG Fang,YANG Hui-zhong
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摘要： 提出了一种基于改进Adaboost的ε不敏感支持向量回归集成算法。该算法使用多个支持向量机按照某种学习规则协调各支持向量机的输出，从而提高其泛化性能。将该方法应用于双酚A生产过程的质量指标软测量建模，仿真结果表明了该集成算法的可行性和有效性。
关键词: 支持向量回归（SVR）, Adaboost算法, 集成算法
Abstract: It proposed an ε-insensitive support vector regression ensemble algorithm based on the improved Adaboost in this paper.Learning by a series of support vector regressions and combining all the results in accordance with some rule，the algorithm improves its regression performance well.Moreover，the proposed algorithm is used in a soft-sensor model for the Bisphenol-A productive process，and the simulation results show the feasibility and effectiveness of the algorithm.
Key words: Support Vector Regression（SVR）, Adaboost algorithm, ensemble algorithm
王 芳,杨慧中. 一种改进的支持向量回归集成算法[J]. 计算机工程与应用, 2008, 44(3): 42-44.
WANG Fang,YANG Hui-zhong. ε-insensitive support vector regression ensemble algorithm based on improved Adaboost[J]. Computer Engineering and Applications, 2008, 44(3): 42-44.
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