Details

Autor(en) / Beteiligte

• Guo, Qin
• Liu, Xianghua
• Ye, Peixin

Titel

The learning performance of the weak rescaled pure greedy algorithms

Ist Teil von

• Journal of inequalities and applications, 2024-03, Vol.2024 (1), p.1-14

Ort / Verlag

Cham: Springer International Publishing

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.