Search Results for author:
Found 11 papers, 0 papers with code
Optimality of Robust Online Learning
In this paper, we study an online learning algorithm with a robust loss function $\mathcal{L}_{\sigma}$ for regression over a reproducing kernel Hilbert space (RKHS).
Online Regularized Learning Algorithm for Functional Data
However, it remains an open problem to obtain capacity independent convergence rates for the estimation error of the unregularized online learning algorithm with decaying step-size.
Capacity dependent analysis for functional online learning algorithms
This article provides convergence analysis of online stochastic gradient descent algorithms for functional linear models.
Coefficient-based Regularized Distribution Regression
Compared with the kernel methods for distribution regression in the literature, the algorithm under consideration does not require the kernel to be symmetric and positive semi-definite and hence provides a simple paradigm for designing indefinite kernel methods, which enriches the theme of the distribution regression.
Realizing data features by deep nets
Based on refined covering number estimates, we find that, to realize some complex data features, deep nets can improve the performances of shallow neural networks (shallow nets for short) without requiring additional capacity costs.
Fast and Strong Convergence of Online Learning Algorithms
In this paper, we study the online learning algorithm without explicit regularization terms.
Convergence of Unregularized Online Learning Algorithms
In this paper we study the convergence of online gradient descent algorithms in reproducing kernel Hilbert spaces (RKHSs) without regularization.
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
Regularization kernel network is an effective and widely used method for nonlinear regression analysis.
Learning from networked examples
Many machine learning algorithms are based on the assumption that training examples are drawn independently.
Guaranteed Classification via Regularized Similarity Learning
In this paper, we propose a regularized similarity learning formulation associated with general matrix-norms, and establish their generalization bounds.
Learning from networked examples in a k-partite graph
Many machine learning algorithms are based on the assumption that training examples are drawn independently.
