Novelty detection: a review - part 2: : neural network based approaches

By Markos Markou, Sameer Singh. 2003

951 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Signal Processing

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Citations in the corpus

Local Novelty Detection in Multi-class Recognition Problems (2015, background): “Moreover, local novelty detection models are more suitable to manage the huge complexity of data distributions induced by features of real-world images.”
A Classification Framework for Anomaly Detection (2005, methodology): “anomalous jet engine vibrations [18, 12, 13], abnormalities in medical data [27, 2], unexpected conditions in engineering [6] and network intrusions [14, 30, 9]. For a recent review of these and other areas of applications as well as many methods for solving the corresponding learning problems we re”
A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques (2008, background): “Recently, Markou and Singh [86, 87] gave a detailed review of novelty detection approaches, using statistical and neural network-based approaches.”
Video-Based Abnormal Human Behavior Recognition—A Review (2012, background): “For example, [47] and [48] present statistical approaches to novelty detection, while [49] and [50] did a comparative”
Attribute-Based Detection of Unfamiliar Classes with Humans in the Loop (2013, background): “In the machine learning literature, there is significant relevant work on novelty detection [21] [22], which includes techniques such as one-class classification methods [34] [5].”
Outlier Detection (2008, background): “A significant portion of the chapter is built upon the material from the articles we have written, in particular [11].”
A review of novelty detection (2014, background): “Markou and Singh distinguished between two main categories of novelty detection techniques: statistical approaches [26] and neural network based approaches [27].”
Toward Supervised Anomaly Detection (2014, background): “…describes the deviations of the new instance compared to the average data instance and, if the deviation exceeds a predeﬁned threshold, the instance is considered an anomaly or an outlier and processed adequately (Markou & Singh, 2003a; Chandola, Banerjee, & Kumar, 2009; Markou & Singh, 2003b).”
Adversarially Learned Anomaly Detection (2018, background): “More recent works are based on deep neural networks; we note that neural networks have a long history of being used for anomaly detection [19].”
Anomaly detection in online social networks (2014, methodology): “…reviews of anomaly detection have provided an overview of the general, non-network based problem, describing the use of various algorithms and the particular types of problems to which these algorithms are most suited (Hodge and Austin, 2004; Chandola et al., 2009; Markou and Singh, 2003b,a).”