Data stream mining is an emergent research area that investigates knowledge extraction from large amounts of continuously generated data, produced by non-stationary distribution. Novelty detection, the ability to identify ...

Supervised learning-based methods can identify crop threats in the visual data collected by an Unmanned Aerial Vehicle (UAV). However, as these methods induce classification models from a finite set of a priori known ...

The ability to detect changes in the data distribution is an important issue in Data Stream mining. Detecting changes in data distribution allows the adaptation of a previously learned model to accommodate the most recent ...

In recent years, we have witnessed the advent of computational systems capable of generating an immense amount of data in a short time period. These applications can be found in areas such as sensor networks, financial ...

There is a growing body of evidence showing that the formation of associative memories is associated with an increase in phosphorylated cAMP response element-binding protein (pCREB) levels. We recently reported that pCREB ...

Exploration and inspection of dynamic environments using mobile robots are applications that benefit immensely from novelty detection algorithms. In this paper we propose the use of a new approach for on-line novelty ...

Mobile robot applications that involve automated exploration and inspection of environments are often dependant on novelty detection, the ability to differentiate between common and uncommon perceptions. Because novelty ...

Mobile robot applications that involve exploration and inspection of dynamic environments benefit, and often even are dependant on reliable novelty detection algorithms. In this paper we compare and discuss the performance ...