Breast cancer is a common disease that can result in death among women. Cancer research
is important because early detection of cancer facilitates clinical practice for patients. The …

The utilization of regression methods employing truncated loss functions is widely praised
for its robustness in handling outliers and representing the solution in the sparse form of the …

Support function machines (SFMs) have been proposed to handle set-valued data, but they
are sensitive to outliers and unstable for re-sampling due to the use of the hinge loss …

Stochastic algorithms are well-known for their performance in the era of big data. In this
article, we study nonsmooth stochastic Difference-of-Convex functions (DC) programs—the …

Least squares projection twin support vector machine (LSPTSVM) has faster computing
speed than classical least squares support vector machine (LSSVM). However, LSPTSVM is …

The previous support vector machine (SVM) including $0/1$ loss SVM, hinge loss SVM,
ramp loss SVM, truncated pinball loss SVM, and others, overlooked the degree of penalty for …

In this study, we propose a novel block coordinate DCA based method for tackling the large-
scale kernel SVM. The proposed method employs a unified scheme that is capable of …

Ensemble learning is a highly efficient method that combines multiple machine learning
models to improve the accuracy and robustness of predictions. Bagging is a popular …

Kernel logistic regression (KLR) is a conventional nonlinear classifier in machine learning.
With the explosive growth of data size, the storage and computation of large dense kernel …

Résumé L'optimisation stochastique revêt une importance majeure à l'ère du big data et de
l'intelligence artificielle. Ceci est attribué à la prévalence de l'aléatoire/de l'incertitude ainsi …