کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6861567 | 1439254 | 2018 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Ramp-loss nonparallel support vector regression: Robust, sparse and scalable approximation
ترجمه فارسی عنوان
رگرسیون بی نهایت پشتیبانی رگرسیونی غیر عادی: محدوده ی قوی، ضعیف و مقیاس پذیر
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Although the twin support vector regression (TSVR) has been extensively studied and diverse variants are successfully developed, when it comes to outlier-involved training set, the regression model can be wrongly driven towards the outlier points, yielding extremely poor generalization performance. To overcome such shortcoming, a Ramp-loss nonparallel support vector regression (RL-NPSVR) is proposed in this work. By adopting Ramp ε-insensitive loss function and another Ramp-type linear loss function, RL-NPSVR can not only explicitly filter noise and outlier suppression but also have an excellent sparseness. The non- convexity of RL-NPSVR is solved by concave-convex programming (CCCP). Because a regularized term is added into each primal problem by rigidly following the structural risk minimization (SRM) principle, CCCP actually solves a series of reconstructed convex optimizations which have the same formulation of dual problem as the standard SVR, so that computing inverse matrix is avoided and SMO-type fast algorithm can be used to accelerate the training process. Numerical experiments on various datasets have verified the effectiveness of our proposed RL-NPSVR in terms of outlier sensitivity, generalization ability, sparseness and scalability.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 147, 1 May 2018, Pages 55-67
Journal: Knowledge-Based Systems - Volume 147, 1 May 2018, Pages 55-67
نویسندگان
Tang Long, Tian Yingjie, Yang Chunyan, Panos M. Pardalos,