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Process."],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p> We study distributed learning with partial coefficients regularization scheme in a reproducing kernel Hilbert space (RKHS). The algorithm randomly partitions the sample set [Formula: see text] into [Formula: see text] disjoint sample subsets of equal size. In order to reduce the complexity of algorithms, we apply a partial coefficients regularization scheme to each sample subset to produce an output function, and average the individual output functions to get the final global estimator. The error bound in the [Formula: see text]-metric is deduced and the asymptotic convergence for this distributed learning with partial coefficients regularization is proved by the integral operator technique. Satisfactory learning rates are then derived under a standard regularity condition on the regression function, which reveals an interesting phenomenon that when [Formula: see text] and [Formula: see text] is small enough, this distributed learning has the same convergence rate with the algorithm processing the whole data in one single machine. <\/jats:p>","DOI":"10.1142\/s021969131850025x","type":"journal-article","created":{"date-parts":[[2018,2,22]],"date-time":"2018-02-22T22:47:55Z","timestamp":1519339675000},"page":"1850025","source":"Crossref","is-referenced-by-count":2,"title":["Distributed learning with partial coefficients regularization"],"prefix":"10.1142","volume":"16","author":[{"given":"Mengjuan","family":"Pang","sequence":"first","affiliation":[{"name":"School of Mathematical Science, University of Jinan, Shandong Provincial Key Laboratory of Network based, Intelligent Computing, Jinan 250022, P. R. 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