| 一种基于训练数据的迭代改进核函数 |
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| 引用本文： | 周志祥,韩逢庆.一种基于训练数据的迭代改进核函数[J].应用数学和力学(英文版),2009,30(1):121-128. |
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| 作者姓名： | 周志祥 韩逢庆 |
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| 作者单位： | Zhi-xiang ZHOU(College of Civil Engineering and Architecture, Chongqing Jiaotong University, Chongqing 400074, P. R.China);Feng-qing HAN(School of Science, Chongqing Jiaotong University, Chongqing 400074, P. R. China)
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| 基金项目： | 国家自然科学基金，the Natural Science Foundation of CQ CSTC |
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| 摘 要： | To improve performance of a support vector regression, a new method for a modified kernel function is proposed. In this method, information of all samples is included in the kernel function with conformal mapping. Thus the kernel function is data-dependent. With a random initial parameter, the kernel function is modified repeatedly until a satisfactory result is achieved. Compared with the conventional model, the improved approach does not need to select parameters of the kernel function. Sim- ulation is carried out for the one-dimension continuous function and a case of strong earthquakes. The results show that the improved approach has better learning ability and forecasting precision than the traditional model. With the increase of the iteration number, the figure of merit decreases and converges. The speed of convergence depends on the parameters used in the algorithm.
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| 关 键 词： | 系统工程 系统分析 预测 技术 |
| 收稿时间： | 2008-07-18 |
An iterative modified kernel based on training data |
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| Zhi-xiang Zhou,Feng-qing Han.An iterative modified kernel based on training data[J].Applied Mathematics and Mechanics(English Edition),2009,30(1):121-128. |
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| Authors: | Zhi-xiang Zhou Feng-qing Han |
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| Institution: | 1. College of Civil Engineering and Architecture,Chongqing Jiaotong University,Chongqing 400074, P. R. China;
2. School of Science, Chongqing Jiaotong University,Chongqing 400074, P. R. China |
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| Abstract: | To improve performance of a support vector regression, a new method for a modified kernel function is proposed. In this method, information of all samples is included in the kernel function with conformal mapping. Thus the kernel function is data-dependent. With a random initial parameter, the kernel function is modified re-peatedly until a satisfactory result is achieved. Compared with the conventional model, the improved approach does not need to select parameters of the kernel function. Sim-ulation is carried out for the one-dimension continuous function and a case of strong earthquakes. The results show that the improved approach has better learning ability and forecasting precision than the traditional model. With the increase of the iteration number, the figure of merit decreases and converges. The speed of convergence depends on the parameters used in the algorithm. |
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| Keywords: | support vector regression data-dependent kernel function iteration |
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