黄贤源, 翟国君, 隋立芬, 黄谟涛. 最小二乘支持向量机在海洋测深异常值探测中的应用[J]. 武汉大学学报 ( 信息科学版), 2010, 35(10): 1188-1191.
引用本文: 黄贤源, 翟国君, 隋立芬, 黄谟涛. 最小二乘支持向量机在海洋测深异常值探测中的应用[J]. 武汉大学学报 ( 信息科学版), 2010, 35(10): 1188-1191.
HUANG Xianyuan, ZHAI Guojun, SUI Lifen, HUANG Motao. Application of Least Square Support Vector Machine to Detecting Outliers of Multi-beam Data[J]. Geomatics and Information Science of Wuhan University, 2010, 35(10): 1188-1191.
Citation: HUANG Xianyuan, ZHAI Guojun, SUI Lifen, HUANG Motao. Application of Least Square Support Vector Machine to Detecting Outliers of Multi-beam Data[J]. Geomatics and Information Science of Wuhan University, 2010, 35(10): 1188-1191.

最小二乘支持向量机在海洋测深异常值探测中的应用

Application of Least Square Support Vector Machine to Detecting Outliers of Multi-beam Data

  • 摘要: 提出了利用最小二乘支持向量机(LS-SVM)构造海底趋势面,利用该趋势面对海洋测深异常值进行剔除的方法,并与趋势面滤波进行了分析和比较,用定理证明趋势面滤波只是LS-SVM取特定参数时的解。实测算例表明,通过调整LS-SVM的参数,使其构造的趋势面更合理,从而有效地剔除测深异常值。

     

    Abstract: In order to solve the problem of trend surface conformation,a new method of constructing trend surface by LS-SVM is presented,and then outliers of Multi-beam data could be eliminated by the trend surface.In order to illuminate the correctness and rationality so a contrast between this method and the approach of trend surface filter.The theorem proves that the trend surface filter is the especial result of LS-SVM.The example shows that in the process of constructing trend surface by LS-SVM,the weight parameters could be adjusted,so the trend surface have the property of popular and steady,the outliers of Multi-beam data could be eliminated effectively.

     

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