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 …

Kernel-based methods for support vector machines (SVM) have shown highly advantageous
performance in various applications. However, they may incur prohibitive computational …

In high-dimensional data classification, effectively extracting discriminative features while
eliminating redundancy is crucial for enhancing the performances of classifiers, such as …

Least squares support vector machines (LSSVMs) have been widely applied for
classification and regression with comparable performance with SVMs. The LSSVM model …

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 …

In this paper, the problem of simultaneously estimating the structure and parameters of
artificial neural networks with multiple hidden layers is considered. A method based on …

Overcomplete latent representations have been very popular for unsupervised feature
learning in recent years. In this paper, we specify which overcomplete models can be …

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 …

Abstract Fixed-Size Least Squares Support Vector Machines (FS-LSSVM) is a powerful tool
for solving large scale classification and regression problems. FS-LSSVM solves an over …

Aiming at machine learning applications in which fast online learning is required, we
develop a variant of the Least Squares SVR (LSSVR) model that can learn incrementally …