کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
407679 | 678161 | 2015 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sequential minimal optimization for SVM with pinball loss
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Sequential minimal optimization for SVM with pinball loss Sequential minimal optimization for SVM with pinball loss](/preview/png/407679.png)
چکیده انگلیسی
To pursue the insensitivity to feature noise and the stability to re-sampling, a new type of support vector machine (SVM) has been established via replacing the hinge loss in the classical SVM by the pinball loss and was hence called a pin-SVM. Though a different loss function is used, pin-SVM has a similar structure as the classical SVM. Specifically, the dual problem of pin-SVM is a quadratic programming problem with box constraints, for which the sequential minimal optimization (SMO) technique is applicable. In this paper, we establish SMO algorithms for pin-SVM and its sparse version. The numerical experiments on real-life data sets illustrate both the good performance of pin-SVMs and the effectiveness of the established SMO methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 149, Part C, 3 February 2015, Pages 1596–1603
Journal: Neurocomputing - Volume 149, Part C, 3 February 2015, Pages 1596–1603
نویسندگان
Xiaolin Huang, Lei Shi, Johan A.K. Suykens,