کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6855137 | 1437607 | 2018 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An effective way to integrate ε-support vector regression with gradients
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
ε-support vector regression (ε-SVR), as a direct implementation of the structural risk minimization principle rather than empirical risk minimization principle, is a new regression method with good generalization ability and can efficiently solve small-sample learning problems. In this work, through incorporating gradient information into the traditional ε-SVR, the gradient-enhanced ε-SVR (GESVR) is developed. The efficiency of GESVR is compared with the traditional ε-SVR by employing analytical function fitting, compared with the gradient-enhanced least square support vector regression (GELSSVR) by using two real-life examples, and tested in a scenario where the exact gradient information is unknown. The results show that GESVR provides more accurate prediction results than the traditional ε-SVR model, and outperforms GELSSVR in some real-life cases.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 99, 1 June 2018, Pages 126-140
Journal: Expert Systems with Applications - Volume 99, 1 June 2018, Pages 126-140
نویسندگان
XiaoJian Zhou, Ting Jiang,