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ACM Computing Surveys (CSUR), ISSN 0360-0300, 07/2009, Volume 41, Issue 3, pp. 1 - 58
Anomaly detection is an important problem that has been researched within diverse research areas and application domains... 
outlier detection | Anomaly detection | Outlier detection | SYSTEM | DISTANCE-BASED OUTLIERS | NOVELTY DETECTION | DENSITY-ESTIMATION | CLASSIFICATION | SELF | Algorithms | UNSUPERVISED OUTLIER DETECTION | INTRUSION-DETECTION | TIME-SERIES | NEURAL-NETWORK | COMPUTER SCIENCE, THEORY & METHODS
Journal Article
The Journal of artificial intelligence research, ISSN 1076-9757, 2013, Volume 46, pp. 235 - 262
Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions... 
SYSTEM | SUPPORT | ATTACKS | NOVELTY DETECTION | CLASSIFICATION | SVMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Computer Science - Learning
Journal Article
Journal of machine learning research, ISSN 1532-4435, 11/2010, Volume 11, pp. 2973 - 3009
A common setting for novelty detection assumes that labeled examples from the nominal class are available, but that labeled examples of novelties are unavailable. The standard ( inductive... 
Two-sample problem | Multiple testing | Semi-supervised learning | Neyman-Pearson classification | Learning reduction | Novelty detection | FALSE DISCOVERY RATE | novelty detection | two-sample problem | multiple testing | CLASSIFICATION | learning reduction | ERROR | semi-supervised learning | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
Journal Article
IEEE Communications Surveys & Tutorials, ISSN 1553-877X, 2010, Volume 12, Issue 2, pp. 159 - 170
.... Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks... 
Temperature sensors | Outlier | Wireless sensor networks | Chemical sensors | Defense industry | Event detection | Acoustic noise | Taxonomy | Acoustic sensors | outlier detection | Intelligent sensors | Guidelines | EWI-18041 | IR-72513 | Outlier Detection | taxonomy | Wireless Sensor Networks | METIS-278690 | Outlier detection | wireless sensor networks | NOVELTY DETECTION | COMPUTER SCIENCE, INFORMATION SYSTEMS | TELECOMMUNICATIONS | Networks | Errors | Noise | Tables (data) | Sensors
Journal Article
Energy and buildings, ISSN 0378-7788, 08/2017, Volume 149, Issue C, pp. 216 - 224
A novel fault detection algorithm based on machine learning is introduced in this paper, that is applied to the detection of faults in heater, ventilation and air conditioning (HVAC) systems... 
HVAC | Gaussian process neural networks | Fault detection | One-class support vector machine | Novelty detection | Air handling unit | DIAGNOSIS | ENGINEERING, CIVIL | CONSTRUCTION & BUILDING TECHNOLOGY | ENERGY & FUELS | SYSTEMS | SUPPORT VECTOR MACHINE | Usage | Algorithms | Neural networks | Analysis | Gaussian processes | Machine learning | Detectors | Models | Mechanical engineering | Electric properties
Journal Article
ACM Computing Surveys (CSUR), ISSN 0360-0300, 05/2019, Volume 52, Issue 2, pp. 1 - 35
Journal Article
Journal Article
Proceedings of the National Academy of Sciences - PNAS, ISSN 0027-8424, 12/2018, Volume 115, Issue 51, pp. 13093 - 13098
Journal Article
Expert systems with applications, ISSN 0957-4174, 01/2018, Volume 91, pp. 374 - 385
....•Random uniform sampling of disjoint image neighborhoods yields background sample.•Detection statistic is distance of remaining neighborhoods from background manifold... 
Manifolds | Manifold learning | Image processing | Anomaly detection | Target detection | SUPPORT | DIMENSIONALITY REDUCTION | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | NOVELTY DETECTION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Equipment and supplies | Algorithms | Data mining | Analysis | Methods
Journal Article
Pattern recognition, ISSN 0031-3203, 03/2015, Volume 48, Issue 3, pp. 659 - 669
... are required in order to decide about initiating adaptive corrections in a timely manner. This paper presents novel methods for covariate shift-detection tests based on a two-stage structure for both univariate and multivariate time-series... 
Non-stationary environments | Covariate shift | Dataset shift-detection | EWMA | NOVELTY DETECTION | CLASSIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | CLASSIFIERS | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
The Artificial intelligence review, ISSN 1573-7462, 2015, Volume 45, Issue 2, pp. 235 - 269
Journal Article