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Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification
Novelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set.
A comparative evaluation of novelty detection algorithms for discrete sequences
The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection.
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