{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T04:50:21Z","timestamp":1764305421937},"reference-count":25,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","funder":[{"name":"Natural Science Foundation of shandong province China","award":["ZR2014AM010"],"award-info":[{"award-number":["ZR2014AM010"]}]},{"name":"National Natural Science Foundation of China (CN)","award":["11671171"],"award-info":[{"award-number":["11671171"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:p> In this paper, we study the performance of kernel-based regression learning with non-iid sampling. The non-iid samples are drawn from different probability distributions with the same conditional distribution. A more general marginal distribution assumption is proposed. Under this assumption, the consistency of the regularization kernel network (RKN) and the coefficient regularization kernel network (CRKN) are proved. Satisfactory capacity independently error bounds and learning rates are derived by the techniques of integral operator. <\/jats:p>","DOI":"10.1142\/s0219691317500072","type":"journal-article","created":{"date-parts":[[2016,11,3]],"date-time":"2016-11-03T02:57:10Z","timestamp":1478141830000},"page":"1750007","source":"Crossref","is-referenced-by-count":2,"title":["Regression learning with non-identically and non-independently sampling"],"prefix":"10.1142","volume":"15","author":[{"given":"Meijian","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, University of Jinan, Shandong Provincial Key Laboratory of Network based Intelligent Computing, Jinan 250022, P. R. 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