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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Liu Yucheng Liu Yubin |
| Copyright Year | 2010 |
| Abstract | As the expansion of the standard Support Vector Machine, compared with the traditional standard Support Vector Machine, the Least Squares Support Vector Machine loses the sparseness of standard Support Vector Machine, which would affect the efficiency of the second study. Aimed at the above puzzle, the article proposed an improved Least Squares Support Vector Machine incremental learning method, using self-adaptive methods to prune the sample, according to the performance of the classifier which each training has been to set the pruning threshold and the increment size of the sample. If you get a good performance of classifier, pruning threshold and sample increment is big, the other hand, if you get a poor performance of classifier, pruning threshold and sample increment is small, resulting in improved efficiency of Least Squares Support Vector Machine training to solve the sparse problem. The simulation experiment results verify the proposed algorithm is feasible. |
| Starting Page | 529 |
| Ending Page | 532 |
| File Size | 332105 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424472796 |
| e-ISBN | 9781424472802 |
| DOI | 10.1109/ICICTA.2010.104 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-05-11 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Machine learning algorithms Automation Computational modeling Educational institutions Support Vector Machine self-adaptive methods Least squares methods Equations Learning systems Support vector machines pruning threshold Support vector machine classification sample incremen Machine learning incremental learning method |
| Content Type | Text |
| Resource Type | Article |
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