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Reducing the number of sub-classifiers for pairwise multi-category support vector machines
- Source :
-
Pattern Recognition Letters . Nov2007, Vol. 28 Issue 15, p2088-2093. 6p. - Publication Year :
- 2007
-
Abstract
- Abstract: Among the SVM-based methods for multi-category classification, “1-a-r”, pairwise and DAGSVM are most widely used. The deficiency of “1-a-r” is long training time and unclassifiable region; the deficiency of pairwise and DAGSVM is the redundancy of sub-classifiers. We propose an uncertainty sampling-based multi-category SVM in this paper. In the new method, some necessary sub-classifiers instead of all N ×(N −1)/2 sub-classifiers are selected to be trained and the uncertainty sampling strategy is used to decide which samples should be selected in each training round. This uncertainty sampling-based method is proved to be accurate and efficient by experimental results on the benchmark data. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 28
- Issue :
- 15
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 26679047
- Full Text :
- https://doi.org/10.1016/j.patrec.2007.06.020