In this paper, we provide a mathematical foundation for the least square regression learning
with indefinite kernel and coefficient regularization. Except for continuity and boundedness …

We prove an oracle inequality for generic regularized empirical risk minimization algorithms
learning from $\a $-mixing processes. To illustrate this oracle inequality, we use it to derive …

Stress is a word used to describe human reactions to emotionally, cognitively and physically
challenging experiences. A hallmark of the stress response is the activation of the autonomic …

The quality of life for upper limb amputees can be greatly improved by the adoption of poly-
articulated myoelectric prostheses. Typically, in these applications, a pattern recognition …

The great success of deep learning poses an urgent challenge to establish the theoretical
basis for its working mechanism. Recently, research on the convergence of deep neural …

In this paper, we study the performance of robust learning with Huber loss. As an alternative
to traditional empirical risk minimization schemes, Huber regression has been extensively …

Stability and repeatability of Pattern Recognition (PR) myoelectric control for upper limb
prosthetic devices remain unresolved challenges in multi-DoFs systems. In this study, we …

Learning from examples is one of the key problems in science and engineering. It deals with
function reconstruction from a finite set of direct and noisy samples. Regularization in …

We present a new oracle inequality for generic regularized empirical risk minimization
algorithms learning from stationary α-mixing processes. Our main tool to derive this …

In this paper, we study the consistency of the regularized least-square regression in a
general reproducing kernel Hilbert space. We characterize the compactness of the inclusion …