Distributed learning for sketched kernel regression

We study distributed learning for regularized least squares regression in a reproducing kernel Hilbert space (RKHS). The divide-and-conquer strategy is a frequently used approach for dealing with very large data sets, which computes an estimate on each subset and then takes an average of the estimat...

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Veröffentlicht in:	Neural networks 2021-11, Vol.143, p.368-376
Hauptverfasser:	Lian, Heng, Liu, Jiamin, Fan, Zengyan
Format:	Artikel
Sprache:	eng
Schlagworte:	Distributed learning Kernel method Optimal rate Randomized sketches
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