کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6856233 1437950 2018 34 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Insensitive stochastic gradient twin support vector machines for large scale problems
ترجمه فارسی عنوان
ماشین های بردار پشتیبانی دوگانه غیرمنتظره تصادفی برای مشکلات بزرگ مقیاس
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Within the large scale classification problem, the stochastic gradient descent method called PEGASOS has been successfully applied to support vector machines (SVMs). In this paper, we propose a stochastic gradient twin support vector machine (SGTSVM) based on the twin support vector machine (TWSVM). Compared to PEGASOS, our method is insensitive to stochastic sampling. Furthermore, we prove the convergence of SGTSVM and the approximation between TWSVM and SGTSVM under uniform sampling, whereas PEGASOS is almost surely convergent and only has an opportunity to obtain an approximation to SVM. In addition, we extend SGTSVM to nonlinear classification problems via a kernel trick. Experiments on artificial and publicly available datasets show that our method has stable performance and can handle large scale problems easily.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 462, September 2018, Pages 114-131
نویسندگان
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