The learning performance of the weak rescaled pure greedy algorithms

Autor:	Qin Guo, Xianghua Liu, Peixin Ye
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Least squares regression Rescaled pure greedy algorithm Weakness sequence K-functional Learning rate Mathematics QA1-939
Zdroj:	Journal of Inequalities and Applications, Vol 2024, Iss 1, Pp 1-14 (2024)
Druh dokumentu:	article
ISSN:	1029-242X
DOI:	10.1186/s13660-024-03077-6
Popis:	Abstract We investigate the regression problem in supervised learning by means of the weak rescaled pure greedy algorithm (WRPGA). We construct learning estimator by applying the WRPGA and deduce the tight upper bounds of the K-functional error estimate for the corresponding greedy learning algorithms in Hilbert spaces. Satisfactory learning rates are obtained under two prior assumptions on the regression function. The application of the WRPGA in supervised learning considerably reduces the computational cost while maintaining its powerful generalization capability when compared with other greedy learning algorithms.
Databáze:	Directory of Open Access Journals
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