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HUANG Xianyuan,ZHAI Guojun,SUI Lifen,CHAI Hongzhou.Study on the detection of abnormal sounding data based on LS-SVM[J].Acta Oceanologica Sinica,2010,(6):115-120
Study on the detection of abnormal sounding data based on LS-SVM
Study on the detection of abnormal sounding data based on LS-SVM
投稿时间:2009-07-29  修订日期:2010-05-27
DOI:10.1007/s13131-010-0082-3
中文关键词:  LS-SVM  trend surface filter  kernel function  abnormal sounding data
英文关键词:LS-SVM  trend surface filter  kernel function  abnormal sounding data
基金项目:The National High-Tech Research and Development Program of China (863 Program) under contract No. 2007AA12Z326; the National Natural Science Foundation of China under contract Nos 40974010 and 40971306.
作者单位E-mail
HUANG Xianyuan Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450052, China
Naval Institute of Hydrographic Surveying and Charting, Tianjin 300061, China 
huangxianyuan007@163.com 
ZHAI Guojun Naval Institute of Hydrographic Surveying and Charting, Tianjin 300061, China  
SUI Lifen Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450052, China  
CHAI Hongzhou Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450052, China
Naval Institute of Hydrographic Surveying and Charting, Tianjin 300061, China 
 
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中文摘要:
      A new method of detecting abnormal sounding data based on LS-SVM is presented. The theorem proves that the trend surface filter is the especial result of LS-SVM. In order to depict the relationship of trend surface filter and LS-SVM, a contrast is given. The example shows that abnormal sounding data could be detected effectively by LS-SVM when the training samples and kernel function are reasonable.
英文摘要:
      A new method of detecting abnormal sounding data based on LS-SVM is presented. The theorem proves that the trend surface filter is the especial result of LS-SVM. In order to depict the relationship of trend surface filter and LS-SVM, a contrast is given. The example shows that abnormal sounding data could be detected effectively by LS-SVM when the training samples and kernel function are reasonable.
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