کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8899393 1631544 2018 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Distributed regression learning with coefficient regularization
ترجمه فارسی عنوان
یادگیری رگرسیون توزیع شده با تنظیم ضریب
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آنالیز ریاضی
چکیده انگلیسی
We study distributed regression learning with coefficient regularization scheme in a reproducing kernel Hilbert space (RKHS). The algorithm randomly partitions the sample set {zi}i=1N into m disjoint sample subsets of equal size, applies the coefficient regularization scheme to each sample subset to produce an output function, and averages the individual output functions to get the final global estimator. We deduce the error bound in expectation in the L2-metric and prove the asymptotic convergence for this distributed coefficient regularization learning. Satisfactory learning rates are then derived under a standard regularity condition on the regression function, which reveals an interesting phenomenon that when m≤Ns and s is small enough, this distributed learning has the same convergence rate compared with the algorithm processing the whole data in one single machine.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Mathematical Analysis and Applications - Volume 466, Issue 1, 1 October 2018, Pages 676-689
نویسندگان
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