Unlabelled data appear in many domains and are particularly relevant to streaming
applications, where even though data is abundant, labelled data is rare. To address the …

Despite decades of research in network traffic analysis and incredible advances in artificial
intelligence, network intrusion detection systems based on machine learning (ML) have yet …

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 …

Often, machine learning applications have to cope with dynamic environments where data
are collected in the form of continuous data streams with potentially infinite length and …

With the rapid development of information technology, data streams in various fields are
showing the characteristics of rapid arrival, complex structure and timely processing …

In this article, we use a Kullback-Leibler random sample partition data model to generate a
set of disjoint data blocks, where each block is a good representation of the entire data set …

Machine learning software accounts for a significant amount of energy consumed in data
centers. These algorithms are usually optimized towards predictive performance, ie …

Machine Learning techniques have been employed in virtually all domains in the past few
years. New applications demand the ability to cope with dynamic environments like data …

Energy consumption reduction has been an increasing trend in machine learning over the
past few years due to its socio-ecological importance. In new challenging areas such as …

Recently machine learning researchers are designing algorithms that can run in embedded
and mobile devices, which introduces additional constraints compared to traditional …