Abstract Support Vector Machine (SVM) has been widely applied in real application due to
its efficient performance in the classification task so that a large number of SVM methods …

Representing data in hyperbolic space can effectively capture latent hierarchical
relationships. To enable accurate classification of points in hyperbolic space while …

The boosted difference of convex functions algorithm (BDCA) was recently proposed for
minimizing smooth difference of convex (DC) functions. BDCA accelerates the convergence …

Twin support vector machine (TWSVM) is an efficient algorithm for binary classification.
However, the lack of the structural risk minimization principle restrains the generalization of …

Recently, spectral kernels have attracted wide attention in complex dynamic environments.
These advanced kernels mainly focus on breaking through the crucial limitation on locality …

Asymmetric kernels naturally exist in real life, eg, for conditional probability and directed
graphs. However, most of the existing kernel-based learning methods require kernels to be …

Laplacian support vector machine (LapSVM) is an extremely popular classification method
and relies on a small number of labels and a Laplacian regularization to complete the …

Multiple kernel learning for feature selection (MKL-FS) utilizes kernels to explore complex
properties of features and performs better in embedded methods. However, the kernels in …

Data hierarchy, as a hidden property of data structure, exists in a wide range of machine
learning applications. A common practice to classify such hierarchical data is first to encode …

Over the past two decades, support vector machines (SVMs) have become a popular
supervised machine learning model, and plenty of distinct algorithms are designed …