A Multi-Classified Method of Support Vector Machine (SVM) Based on Entropy
2012 ◽
Vol 241-244
◽
pp. 1629-1632
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Keyword(s):
Studies propose to combine standard SVM classification with the information entropy to increase SVM classification rate as well as reduce computational load of SVM testing. The algorithm uses the information entropy theory to per-treat samples’ attributes, and can eliminate some attributes which put small impacts on the date classification by introducing the reduction coefficient, and then reduce the amount of support vectors. The results show that this algorithm can reduce the amount of support vectors in the process of the classification with support vector machine, and heighten the recognition rate when the amount of the samples is larger compared to standard SVM and DAGSVM.
2017 ◽
Vol 2017
◽
pp. 1-11
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2014 ◽
Vol 615
◽
pp. 194-197
2017 ◽
Vol 2017
◽
pp. 1-16
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2015 ◽
Vol 13
(2)
◽
pp. 50-58
2021 ◽
Vol 9
(8)
◽
pp. 1021-1026
2020 ◽
Vol 17
(4)
◽
pp. 572-578