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 …

Abstract Machine learning (ML) is continuously unleashing its power in a wide range of
applications. It has been pushed to the forefront in recent years partly owing to the advent of …

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 …

Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

In malicious URLs detection, traditional classifiers are challenged because the data volume
is huge, patterns are changing over time, and the correlations among features are …

Machine learning can provide deep insights into data, allowing machines to make high-
quality predictions and having been widely used in real-world applications, such as text …

This article considers predicting future statuses of multiple agents in an online fashion by
exploiting dynamic interactions in the system. We propose a novel collaborative prediction …

Nowadays, datasets are always dynamic and patterns in them are changing. Instances with
different labels are intertwined and often linearly inseparable, which bring new challenges to …

Gait recognition enables many potential applications requiring identification. Wi-Fi-based
gait recognition is predominant because of its noninvasive and ubiquitous advantages …