The twin support vector machine (TSVM) is widely used in classification problems, but it is
not efficient enough for large-scale data sets. Furthermore, to get the optimal parameter, the …

Structural twin support vector machine (S-TSVM) performs better than TSVM, since it
incorporates the structural information of the corresponding class into the model. However …

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

This paper proposes a novel L 1-norm loss based twin support vector machine (L1LTSVM)
classifier for binary recognition. In this L1LTSVM, each optimization problem simultaneously …

Along with the constantly increasing complexity of industrial automation systems, machine
learning methods have been widely applied to detecting abnormal states in such systems …

To make support vector machine (SVM) applicable to large-scale data sets, safe screening
rules are developed recently. The main idea is to reduce the scale of SVM by safely …

Support vector regression (SVR) is popular and efficient for regression problems. However,
it is time-consuming to solve it, especially for large datasets. Inspired by the sparse solutions …

The dual coordinate descent (DCD) algorithm solves the dual problem of support vector
machine (SVM) by minimizing a series of single-variable sub-problems with a random order …

Nonparametric modeling is a commonly used data-driven method for modeling ship
maneuvering motion, and its performance depends on reliable training data. The training …

In this brief, we give a new proof of the asymptotic convergence of the sequential minimum
optimization (SMO) algorithm for both the most violating pair and second order rules to …