کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6940866 | 870309 | 2016 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Support vector number reduction by extending iterative preimage addition using genetic algorithm-based preimage estimation
ترجمه فارسی عنوان
کاهش تعداد بردار حروف با افزودن پیش نمایش تکراری با استفاده از برآورد پیش نمایش بر اساس الگوریتم ژنتیکی
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
As the support vector (SV) number of a support vector machine (SVM) determines the execution speed of the testing phase, there have been diverse methods to reduce it. Although iterative preimage addition (IPA), belonging to the 'reduced set construction', is reported to be able to reduce a large portion of the SV number of a standard SVM when the kernel is a radial basis function (RBF), the fact that it cannot be applied to other types of kernels is a significant drawback. To address this problem, this paper proposes a novel genetic algorithm-based preimage estimation method and incorporates it into a conventional IPA such that all types of kernels can be handled. Experimental results indicate that the proposed method shows the equivalent performance to the conventional IPA when an RBF kernel is used. Furthermore, they show that the proposed method can reduce the SV number of the histogram of oriented gradient (HOG) feature-based pedestrian classifier using linear, quadratic, and sigmoid kernels by more than 99.5%.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 84, 1 December 2016, Pages 43-48
Journal: Pattern Recognition Letters - Volume 84, 1 December 2016, Pages 43-48
نویسندگان
Jung Ho Gi,