کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4944433 | 1437990 | 2017 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Online pairwise learning algorithms with convex loss functions
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Online pairwise learning algorithms with general convex loss functions without regularization in a Reproducing Kernel Hilbert Space (RKHS) are investigated. Under mild conditions on loss functions and the RKHS, upper bounds for the expected excess generalization error are derived in terms of the approximation error when the stepsize sequence decays polynomially. In particular, for Lipschitz loss functions such as the hinge loss, the logistic loss and the absolute-value loss, the bounds can be of order O(Tâ13logT) after T iterations, while for the least squares loss, the bounds can be of order O(Tâ14logT). In comparison with previous works for these algorithms, a broader family of convex loss functions is studied here, and refined upper bounds are obtained.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volumes 406â407, September 2017, Pages 57-70
Journal: Information Sciences - Volumes 406â407, September 2017, Pages 57-70
نویسندگان
Junhong Lin, Yunwen Lei, Bo Zhang, Ding-Xuan Zhou,