A unifying review of deep and shallow anomaly detection
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 …
the art in detection performance on complex data sets, such as large collections of images or …
Introduction to machine learning for brain imaging
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 …
become a working horse in brain imaging and the computational neurosciences, as they are …
[HTML][HTML] Loda: Lightweight on-line detector of anomalies
T Pevný - Machine Learning, 2016 - Springer
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 …
strong classifier with error rates similar to those of more sophisticated methods. In …
Automatic analysis of malware behavior using machine learning
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 …
systems. The amount and diversity of its variants render classic security defenses ineffective …
Learning intrusion detection: supervised or unsupervised?
Application and development of specialized machine learning techniques is gaining
increasing attention in the intrusion detection community. A variety of learning techniques …
increasing attention in the intrusion detection community. A variety of learning techniques …
Applicability domains for classification problems: benchmarking of distance to models for Ames mutagenicity set
I Sushko, S Novotarskyi, R Körner… - Journal of chemical …, 2010 - ACS Publications
The estimation of accuracy and applicability of QSAR and QSPR models for biological and
physicochemical properties represents a critical problem. The developed parameter of …
physicochemical properties represents a critical problem. The developed parameter of …
[HTML][HTML] Towards zero training for brain-computer interfacing
Electroencephalogram (EEG) signals are highly subject-specific and vary considerably even
between recording sessions of the same user within the same experimental paradigm. This …
between recording sessions of the same user within the same experimental paradigm. This …
Chemoinformatic classification methods and their applicability domain
M Mathea, W Klingspohn, K Baumann - Molecular Informatics, 2016 - Wiley Online Library
Classification rules are often used in chemoinformatics to predict categorical properties of
drug candidates related to bioactivity from explanatory variables, which encode the …
drug candidates related to bioactivity from explanatory variables, which encode the …
[HTML][HTML] Towards explaining anomalies: a deep Taylor decomposition of one-class models
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 …
applications in the sciences and industry. In practice, it is not always sufficient to reach high …
An experimental evaluation of novelty detection methods
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 …
conditions rarely occur and knowledge about novelty in that system is often limited or …