In this paper, we propose a robust smooth pinball loss nonparallel support vector machine
(SpinNSVM) for binary classification. We first define a smooth pinball loss function, which is …

Hysteresis nonlinearity of piezoelectric actuators degrades the positioning accuracy of
micro/nanopositioning systems. To overcome this problem, an innovative hysteresis …

Hysteresis effect degrades the positioning accuracy of a piezostage, and hence the
nonlinearity has to be suppressed for ultrahigh-precision positioning applications. This …

This paper develops a novel hybrid approach that integrates metamodeling, machine
learning algorithms, and a variance decomposition technique to support global uncertainty …

Least squares support vector machine (LS-SVM) is a popular hyperplane-based classifier
and has attracted many attentions. However, it may suffer from singularity or ill-condition …

Impact force magnitude detection and site location for clamped plates have direct relevance
to the maintenance of aircraft and spacecraft structures. This article presents the impact …

Inspired by the recently developed Compressed Sensing (CS) theory, this study advances a
sparse Spatial-Spectral Least Square Support Vector Machine (SS-LSSVM) for …

A least squares support vector machine (LS-SVM) offers performance comparable to that of
SVMs for classification and regression. The main limitation of LS-SVM is that it lacks sparsity …

Among the support vector machines, Least Square Support Vector Machine (LSSVM) is
computationally attractive for reducing a set of inequality constraints to linear equations …

The kernel method is a tool that converts data to a kernel space where operation can be
performed. When converted to a high-dimensional feature space by using kernel functions …