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A Simplification to Support Vector Machine for the Second Training
Hangzhou, China November 29-December 01
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICAT.2006.2716th International Conference on Arti ...
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Jinglong Fang, Hangzhou Dianzi University, China
Shuo Chen, Hangzhou Dianzi University, China
Zhigeng Pan, Zhejiang University, China
Yigang Wang, Hangzhou Dianzi University, China
For complicated recognition problem, the number of support vectors is large and recognition speed is low, because some sample were divided into section by error this time. To solve this problem, a method is bought to simplify the support vector machines based the minimal misestimate margin idea. Experiments show that this new support vector machine not only reduces the number of support vectors and recognition time but also has the same accuracy as (even better than) traditional support vector machine.
Citation:
Jinglong Fang, Shuo Chen, Zhigeng Pan, Yigang Wang, "A Simplification to Support Vector Machine for the Second Training," icat, pp.73-78, 16th International Conference on Artificial Reality and Telexistence--Workshops (ICAT'06), 2006
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