Most of the currently used techniques for linear system identification are based on classical
estimation paradigms coming from mathematical statistics. In particular, maximum likelihood …

Loss function plays a vital role in supervised learning frameworks. The selection of the
appropriate loss function holds the potential to have a substantial impact on the proficiency …

Multi-target regression has recently regained great popularity due to its capability of
simultaneously learning multiple relevant regression tasks and its wide applications in data …

Multi-label classification studies the task where each example belongs to multiple labels
simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) …

Loss function is a critical component of machine learning. Some robust loss functions are
proposed to mitigate the adverse effects caused by noise. However, they still face many …

This paper presents a kernel-based discriminative learning framework on probability
measures. Rather than relying on large collections of vectorial training examples, our …

Multi-view imbalanced learning concentrates on recognizing valuable patterns from multi-
view imbalanced data. There are numerous algorithm-level multi-view imbalanced learning …

Conventional regression methods often fail when encountering noise. The application of a
bounded loss function is an effective means to enhance regressor robustness. However …

In the domain of machine learning, the significance of the loss function is paramount,
especially in supervised learning tasks. It serves as a fundamental pillar that profoundly …

Least squares support vector machine (LSSVM) considerably simplifies problem solving,
however, there are restrictions. The first is that it treats samples on both sides of the proximal …