Distributed learning with regularized least squares
We study distributed learning with the least squares regularization scheme in a reproducing
kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a …
kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a …
Distributed kernel-based gradient descent algorithms
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 …
conquer approach and gradient descent algorithm in a reproducing kernel Hilbert space …
Online pairwise learning algorithms
Pairwise learning usually refers to a learning task that involves a loss function depending on
pairs of examples, among which the most notable ones are bipartite ranking, metric learning …
pairs of examples, among which the most notable ones are bipartite ranking, metric learning …
Rates of convergence of randomized Kaczmarz algorithms in Hilbert spaces
Abstract Recently, the Randomized Kaczmarz algorithm (RK) draws much attention because
of its low computational complexity and less requirement on computer memory. Many …
of its low computational complexity and less requirement on computer memory. Many …
Optimal convergence for distributed learning with stochastic gradient methods and spectral algorithms
We study generalization properties of distributed algorithms in the setting of nonparametric
regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed …
regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed …
Online learning algorithms can converge comparably fast as batch learning
Online learning algorithms in a reproducing kernel Hilbert space associated with convex
loss functions are studied. We show that in terms of the expected excess generalization …
loss functions are studied. We show that in terms of the expected excess generalization …
Self-paced learning-assisted regularization reconstruction method with data-adaptive prior for electrical capacitance tomography
J Lei, Q Liu - Expert Systems with Applications, 2022 - Elsevier
The advent of electrical capacitance tomography provides a potential visualization-based
measurement paradigm for process monitoring, but the large gap between the reconstructed …
measurement paradigm for process monitoring, but the large gap between the reconstructed …
Convergence of gradient descent for minimum error entropy principle in linear regression
We study the convergence of minimum error entropy (MEE) algorithms when they are
implemented by gradient descent. This method has been used in practical applications for …
implemented by gradient descent. This method has been used in practical applications for …
[HTML][HTML] Analysis of regularized federated learning
Federated learning is an efficient machine learning tool for dealing with heterogeneous big
data and privacy protection. Federated learning methods with regularization can control the …
data and privacy protection. Federated learning methods with regularization can control the …
Randomized Kaczmarz with tail averaging
The randomized Kaczmarz (RK) method is a well-known approach for solving linear least-
squares problems with a large number of rows. RK accesses and processes just one row at …
squares problems with a large number of rows. RK accesses and processes just one row at …