Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning
setting, which requires the entire training data to be made available prior to the learning …

Malicious URL, aka malicious website, is a common and serious threat to cybersecurity.
Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure …

Kernel-based methods exhibit well-documented performance in various nonlinear learning
tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task …

Since support vector regression (SVR) is a flexible regression algorithm, its computational
complexity does not depend on the dimensionality of the input space, and it has excellent …

Structural equation models (SEMs) and vector autoregressive models (VARMs) are two
broad families of approaches that have been shown useful in effective brain connectivity …

The visual perception systems aim to autonomously collect consecutive visual data and
perceive the relevant information online like human beings. In comparison with the classical …

Kernel-based regression represents an important family of learning techniques for solving
challenging regression tasks with non-linear patterns. Despite being studied extensively …

Classifying binary imbalanced streaming data is a significant task in both machine learning
and data mining. Previously, online area under the receiver operating characteristic (ROC) …

Multi-kernel learning (MKL) exhibits well-documented performance in online non-linear
function approximation. Federated learning enables a group of learners (called clients) to …