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Content Provider | IEEE Xplore Digital Library |
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Author | Li Zhang Xiangru Meng Hua Zhou |
Copyright Year | 2008 |
Abstract | A new method based on kernel which can measure class separability in feature space is proposed in this paper for existing error accumulation when the Hierarchical SVMs is used to diagnose multi-class network fault. This method has defined metrics of sample distribution in feature space, which are used as the rule of constructing Hierarchical SVMs. Experiment results show that this method can restrain error accumulation and has higher multi-class classification accuracy, and offer an effective way for Network fault diagnosis. |
Starting Page | 753 |
Ending Page | 756 |
File Size | 332801 |
Page Count | 4 |
File Format | |
ISBN | 9780769535432 |
DOI | 10.1109/WKDD.2009.79 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-01-23 |
Publisher Place | Russia |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Knowledge engineering Extraterrestrial measurements Data engineering Data mining Hierarchical SVMs Fault diagnosis Support vector machines Network Fault Diagnosis Kernel Method Support vector machine classification Machine learning Kernel Artificial intelligence |
Content Type | Text |
Resource Type | Article |
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