کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
535180 | 870327 | 2007 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Reducing the number of sub-classifiers for pairwise multi-category support vector machines
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 28, Issue 15, 1 November 2007, Pages 2088–2093
Journal: Pattern Recognition Letters - Volume 28, Issue 15, 1 November 2007, Pages 2088–2093
نویسندگان
Wang Ye, Huang Shang-Teng,