A novel non-parallel hyperplane Universum support vector machine (U-NHSVM) is
proposed in this paper. Universum data with ensconced prior knowledge are exploited by a …

In recent years, self-attention has become the dominant paradigm for sequence modeling in
a variety of domains. However, in domains with very long sequence lengths the $\mathcal …

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 …

Direct multi-task twin support vector machine (DMTSVM) is an effective algorithm to deal
with multi-task classification problems. However, the generated hyperplane may shift to …

Multi-view learning aims to make use of the advantages of different views to complement
each other and fully mines the potential information in the data. However, the complexity of …

Direct multi-task twin support vector machine (DMTSVM) obtains great performance in
dealing with correlated tasks. However, DMTSVM only considers the empirical risk …

The-regularized logistic regression (L1-LR) is popular for classification problems. To
accelerate its training speed for high-dimensional data, techniques named safe screening …

By reducing optimization to a sequence of small subproblems, working set methods achieve
fast convergence times for many challenging problems. Despite excellent performance …

In this paper, we present a new classifier called elastic net twin support vector machine
(ETSVM). It resolves two smaller-sized quadratic programming problems (QPPs) similarly to …

The problem of learning a sparse model is conceptually interpreted as the process of
identifying active features/samples and then optimizing the model over them. Recently …