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 …

Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over
time. This dynamic behavior is either caused by external factors or initiated by the system …

Federated learning (FL) is a machine learning paradigm where a shared central model is
learned across distributed devices while the training data remains on these devices …

LIBOL is an open-source library for large-scale online learning, which consists of a large
family of efficient and scalable state-of-the-art online learning algorithms for large-scale …

Recent years have witnessed extensive studies on distance metric learning (DML) for
improving similarity search in multimedia information retrieval tasks. Despite their …

In this paper, we propose a novel machine learning framework called “Online Transfer
Learning”(OTL), which aims to attack an online learning task on a target domain by …

Transfer learning techniques have been broadly applied in applications where labeled data
in a target domain are difficult to obtain while a lot of labeled data are available in related …

We present a three-part toolkit for developing morphological analyzers for languages
without natural word boundaries. The first part is a C++ 11/14 lattice-based morphological …

The potentially detrimental effects of cyberbullying have led to the development of numerous
automated, data-driven approaches, with emphasis on classification accuracy …

Malicious Uniform Resource Locator (URL) detection is an important problem in web search
and mining, which plays a critical role in internet security. In literature, many existing studies …