The rapid emergence of massive datasets in various fields poses a serious challenge to
traditional statistical methods. Meanwhile, it provides opportunities for researchers to …

Principal component analysis (PCA) is fundamental to statistical machine learning. It extracts
latent principal factors that contribute to the most variation of the data. When data are stored …

Learning rates for least-squares regression are typically expressed in terms of L 2-norms. In
this paper we extend these rates to norms stronger than the L 2-norm without requiring the …

This paper provides error analysis for distributed semi-supervised learning with kernel ridge
regression (DSKRR) based on a divide-and-conquer strategy. DSKRR applies kernel ridge …

In the misspecified spectral algorithms problem, researchers usually assume the
underground true function $ f_ {\rho}^{*}\in [\mathcal {H}]^{s} $, a less-smooth interpolation …

We study the generalization ability of distributed learning equipped with a divide-and-
conquer approach and gradient descent algorithm in a reproducing kernel Hilbert space …

This paper focuses on generalization performance analysis for distributed algorithms in the
framework of learning theory. Taking distributed kernel ridge regression (DKRR) for …

Previous analysis of regularized functional linear regression in a reproducing kernel Hilbert
space (RKHS) typically requires the target function to be contained in this kernel space. This …

Stochastic gradient descent (SGD) has become the method of choice to tackle large-scale
datasets due to its low computational cost and good practical performance. Learning rate …

We study generalization properties of distributed algorithms in the setting of nonparametric
regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed …