We propose three different methods to determine the optimal number of hidden nodes
based on L 1 regularization for a multilayer perceptron network. The first two methods …

Lasso regression, known for its efficacy in high-dimensional data analysis and feature
selection, stands as a cornerstone in the realm of supervised learning for regression …

A resilient system design problem is formulated as the quantification of uncommitted
reconfigurable resources required for a system of components to survive its lifetime within …

Federated learning that is an approach to addressing the “data silo” problem in a
collaborative fashion may face the risk of data leakage in real-world contexts. To solve this …

Subsampling is an important technique to tackle the computational challenges brought by
big data. Many subsampling procedures fall within the framework of importance sampling …

Modal regression, a widely used regression protocol, has been extensively investigated in
statistical and machine learning communities due to its robustness to outlier and heavy …

Subsampling is a widely used and effective method to deal with the challenges brought by
big data. Most subsampling procedures are designed based on the importance sampling …

Big data research has become a globally hot topic in recent years. One of the core problems
in big data learning is how to extract effective information from the huge data. In this paper …

We analyze a least square regularized regression (LSRR) problem with multiscale kernels
on the assumption that observations are subject to be non-independent and identically …

This paper considers a ℓ 1-coefficient regularized regression algorithm with multiscale
kernels based on non-independent and identically distributed (non-iid) samples. The data …