The prevalence of mobile phones, the internet-of-things technology, and networks of
sensors has led to an enormous and ever increasing amount of data that are now more …

Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …

In Cloud Ethics Louise Amoore examines how machine learning algorithms are transforming
the ethics and politics of contemporary society. Conceptualizing algorithms as ethicopolitical …

With the emergence of the big data age, the issue of how to obtain valuable knowledge from
a dataset efficiently and accurately has attracted increasingly attention from both academia …

We introduce an ensemble of classifiers-based approach for incremental learning of concept
drift, characterized by nonstationary environments (NSEs), where the underlying data …

Learning from data streams in the presence of concept drift is among the biggest challenges
of contemporary machine learning. Algorithms designed for such scenarios must take into …

Big Data is one of the major challenges of statistical science and has numerous
consequences from algorithmic and theoretical viewpoints. Big Data always involve massive …

Learning in nonstationary environments, also known as learning concept drift, is concerned
with learning from data whose statistical characteristics change over time. Concept drift is …

The increasing use of smartphones as personal computing platforms to access personal
information has stressed the demand for secure and usable authentication techniques, and …

There have been research activities in the area of keystroke dynamics biometrics on
physical keyboards (desktop computers or conventional mobile phones) undertaken in the …