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 and data mining techniques have been used in numerous real-
world applications. An assumption of traditional machine learning methodologies is the …

Transfer learning (TL) has been successfully applied to many real-world problems that
traditional machine learning (ML) cannot handle, such as image processing, speech …

The evolutionary algorithm (EA) is a nature-inspired population-based search method that
works on Darwinian principles of natural selection. Due to its strong search capability and …

When labeled data is scarce for a specific target task, transfer learning often offers an
effective solution by utilizing data from a related source task. However, when transferring …

The aim of this paper is to give an overview of domain adaptation and transfer learning with
a specific view on visual applications. After a general motivation, we first position domain …

This is the third and final paper in a series laying foundations for a theory/methodology of
Population-Based Structural Health Monitoring (PBSHM). PBSHM involves utilising …

Modern multilingual models are trained on concatenated text from multiple languages in
hopes of conferring benefits to each (positive transfer), with the most pronounced benefits …

(1) Background: Transfer learning refers to machine learning techniques that focus on
acquiring knowledge from related tasks to improve generalization in the tasks of interest. In …

Medical image processing is one of the most important topics in the Internet of Medical
Things (IoMT). Recently, deep learning methods have carried out state-of-the-art …