Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Machine learning and pattern recognition algorithms have in the past years developed to
become a working horse in brain imaging and the computational neurosciences, as they are …

In supervised learning it has been shown that a collection of weak classifiers can result in a
strong classifier with error rates similar to those of more sophisticated methods. In …

Malicious software–so called malware–poses a major threat to the security of computer
systems. The amount and diversity of its variants render classic security defenses ineffective …

Application and development of specialized machine learning techniques is gaining
increasing attention in the intrusion detection community. A variety of learning techniques …

The estimation of accuracy and applicability of QSAR and QSPR models for biological and
physicochemical properties represents a critical problem. The developed parameter of …

Electroencephalogram (EEG) signals are highly subject-specific and vary considerably even
between recording sessions of the same user within the same experimental paradigm. This …

Classification rules are often used in chemoinformatics to predict categorical properties of
drug candidates related to bioactivity from explanatory variables, which encode the …

Detecting anomalies in the data is a common machine learning task, with numerous
applications in the sciences and industry. In practice, it is not always sufficient to reach high …

Novelty detection is especially important for monitoring safety-critical systems in which novel
conditions rarely occur and knowledge about novelty in that system is often limited or …