| 水资源短缺风险的支持向量机应用研究 |
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| 引用本文： | 牛海军,蔡春.水资源短缺风险的支持向量机应用研究[J].辽宁科技学院学报,2013(4):23-26. |
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| 作者姓名： | 牛海军 蔡春 |
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| 作者单位： | 铁岭师范高等专科学校师范学院,辽宁铁岭,112001;北京联合大学应用文理学院,北京,100191 |
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| 基金项目： | 北京市教育委员会项目（km201111417004）,北京市骨干教师人才强教项目（PHR201008292） |
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| 摘 要： | 分析了支持向量机特征提取算法，支持向量机算法的优点是在小样本、非线性以及高维度模式识别中拥有的最优性能。随着近几年北京市水资源日益短缺的严峻事实，本文将支持向量机特征提取算法应用到北京市水资源短缺风险的主要因素提取方面，此分析对水资源短缺分享的研究对维护社会经济的稳定、可持续发展战略的实施具有重要意义。
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| 关 键 词： | 支持向量机 特征提取 短缺风险 |
Research into the Application of Support Vector Machine on the Risk of Water Resource Shortage |
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| NIU Haijunl ; CAI Chun.Research into the Application of Support Vector Machine on the Risk of Water Resource Shortage[J].Journal of Liaoning Institute of Science and Technology,2013(4):23-26. |
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| Authors: | NIU Haijunl ; CAI Chun |
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| Institution: | NIU Haijunl ; CAI Chun2 (1. Tieling Normal College, Tieling, Liaoning, 112001; 2. College of Art and Science, Beijing United University, Beijing, 100191 ) |
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| Abstract: | The superioyity of support vector machine is the dominant position at simulated distinguishability from small sample, nonlin- ear and high dimentional pattern. Considering the severe riality of growing water scarcity in Beijing, the paper takes the feature extrac- tion algorithm used to the risk factors extraction of water resource. It is of great significance for maintaining social economy and continu- ous development to analyse with support vector machine. |
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| Keywords: | Support vector machine Feature extraction Shortage risk |
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