Online Learning by SVM for Multiclass Classification in Communication Network Traffic Management
碩士 === 國立交通大學 === 統計學研究所 === 104 === Supervised learning based on the method of support vector machine (SVM) is very useful for the classification of complex data. However, the computation cost is very high when the training dataset is massive. Online learning problems will need to handle the proble...
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| Format: | Others |
| Language: | en_US |
| Published: |
2016
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| Online Access: | http://ndltd.ncl.edu.tw/handle/78985003628710919608 |
| Summary: | 碩士 === 國立交通大學 === 統計學研究所 === 104 === Supervised learning based on the method of support vector machine (SVM) is very useful for the classification of complex data. However, the computation cost is very high when the training dataset is massive. Online learning problems will need to handle the problems of memory limitation and computational complexity. In this study, the online learning methods by SVM for multiclass problems in massive data are developed. The empirical performance of these methods will be evaluated by real data in communication network traffic management.
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