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The computational complexity of the RPGLA is less than the Orthogonal Greedy Learning Algorithm (OGLA) and Relaxed Greedy Learning Algorithm (RGLA). We obtain the convergence rates of the RPGLA for continuous kernels. When the kernel is infinitely smooth, we derive a convergence rate that can be arbitrarily close to the best rate [Formula: see text] under a mild assumption of the regression function. <\/jats:p>","DOI":"10.1142\/s0219691322500485","type":"journal-article","created":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T05:50:52Z","timestamp":1666936252000},"source":"Crossref","is-referenced-by-count":6,"title":["Optimality of the rescaled pure greedy learning algorithms"],"prefix":"10.1142","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6202-8311","authenticated-orcid":false,"given":"Wenhui","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, P. R. 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