Chapter 13: Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware systems
Carlo Regazzoni; Ali Krayani; Giulia Slavic; Lucio Marcenaro DITEN, University of Genoa, Genoa, Italy
Abstract
Anomaly detection techniques constitute a fundamental resource in many applications such as medical image analysis, fraud detection or video surveillance. These techniques represent an essential step also for artificial self-aware systems that can continually learn from new situations. In this chapter, we present a semisupervised method for the detection of anomalies for this type of self-aware agents. The described method leverages the message-passing capability of Generalized Dynamic Bayesian Networks (GDBNs) ...
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