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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xin Chunlin Gao Ningning Shen Fengwu |
| Copyright Year | 2010 |
| Description | Author affiliation: School of Economics and Management, Beijing University of Chemical Technology, China (Gao Ningning; Shen Fengwu) || School of Economics and Management, Beijing University of Chemical Technology, Research Center for Operations Management and Strategic Decision, China (Xin Chunlin) |
| Abstract | Support vector machine (SVM) algorithms were introduced to analyze the quality of seawater, and two models were constructed to analyze different seawater qualities, one is the SVM model for recognizing two kinds of seawater and the other is hierarchical support vector machines (H-SVMs) model for recognizing multi-seawater. The decision function of the first model for recognizing two kinds of seawater was applied to assess the unknown seawater samples. The trial results were consistent with the expected. It could be concluded that the parameter w in the decision functions is able to describe the weights of evaluation indices of seawater quality, which is much easier to determine the weights than fuzzy synthesis assessment (FSA). All show that SVM are based on a strict mathematical theory with a simple structure and a good generalization performance, which are worth being studied to assess seawater quality. |
| File Size | 269495 |
| File Format | |
| ISBN | 9781424472352 |
| e-ISBN | 9781424472376 |
| DOI | 10.1109/ICCASM.2010.5620574 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-10-22 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Training H-SVMs Fitting FSA Decision function Seawaterquality assessment SVM Mathematical model Kernel Monitoring Board of Directors |
| Content Type | Text |
| Resource Type | Article |
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