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加权支持向量回归算法在运力预测中的应用
引用本文:李冬琴,王呈方,王丽铮.加权支持向量回归算法在运力预测中的应用[J].江苏船舶,2007,24(4):1-4.
作者姓名:李冬琴  王呈方  王丽铮
作者单位:1. 武汉理工大学交通学院
2. 武汉理工大学
摘    要:支持向量机(Support Vector Machines,SVM)是基于统计学习理论框架下的一种新的通用机器学习方法,是一种处理非线性分类和非线性回归的有效方法。由于具有完备的理论基础和出色的学习性能,该技术已成为当前国际机器学习界的研究热点,能较好地解决小样本、高维数、非线性和局部极小点等实际问题。近来,SVR方法被引入求解回归和预测问题,并在各领域中得到广泛的应用。本文在分析现有基于高斯核的支持向量回归方法优缺点的基础上,突破目前在构造支持向量机中存在的"所有支持向量与样本之间的在特征空间中的内积所对应的核函数参数一定要相等"的这一思维定势,提出了一种新的算法——"基于高斯核参数加权的支持向量回归机"算法,并将该算法应用在世界散货船队运力预测中。估算结果证明了这种改进的支持向量回归算法在船队运力预测中的有效性和实用性。

关 键 词:加权支持向量机回归  权重因子  散货船队  运力预测
修稿时间:2007-03-16

Application of weighted support vector regression algorithm in transport capacity forecasting
Li Dongqin,Wang Chengfang,Wang Lizheng.Application of weighted support vector regression algorithm in transport capacity forecasting[J].Jiangsu Ship,2007,24(4):1-4.
Authors:Li Dongqin  Wang Chengfang  Wang Lizheng
Abstract:The Support Vector Machines(SVM),a new general machine learning method based on the frame of statistical learning theory,is an effective method of processing the non-liner classification and regression.Because of its self-contained theoretical background and excellent generalization performance,it has become the hotspot of international machine learning currently.This method can solve those practical problems such as limited samples、high dimension、non-linear problem and local minimum.Recently,Support Vector Regression(SVM) has been introduced to solve regression and prediction problems and widely used in many fields.With the analysis of both advantages and disadvantages of current support vector regression algorithm based on Gaussian kernel function,we propose a new weighted support vector regression algorithm in this article,thus the rigorous constraint is overcome that maintains "corresponding parameters of kernel function support vectors should be equal",And this new algorithm is used in the transport capacity forecasting.The results of experiments show the practicability and effectiveness of this algorithm proposed in this article in the transport capacity forecasting.
Keywords:
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