| HUANG Xianyuan,ZHAI Guojun,SUI Lifen,CHAI Hongzhou. 2010. Study on the detection of abnormal sounding data based on LS-SVM. Acta Oceanologica Sinica, (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 |
| Received:July 29, 2009 Revised:May 27, 2010 |
| DOI：10.1007/s13131-010-0082-3 |
| Key words:LS-SVM trend surface filter kernel function abnormal sounding data |
| 中文关键词: 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. |
| Author Name | Affiliation | 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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| Abstract: |
| 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. |
| 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. |
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