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Anomaly Detection
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Geliefert zwischen Fr., 27.02.2026 und Mo., 02.03.2026
Details
High Quality Content by WIKIPEDIA articles! High Quality Content by WIKIPEDIA articles! Anomaly Detection refers to detecting patterns in a given data set that do not conform to an established normal behavior. The patterns thus detected are called anomalies and often translate to critical and actionable information in several application domains. Anomalies are also referred to as outliers, surprise, aberrant, deviation, peculiarity, etc. Three broad categories of anomaly detection techniques exist. Supervised anomaly detection techniques learn a classifier using labeled instances belonging to normal and anomaly class, and then assign a normal or anomalous label to a test instance. Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the learnt model. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that majority of the instances in the data set are normal.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09786131171444
- Editor Lambert M. Surhone, Miriam T. Timpledon, Susan F. Marseken
- EAN 9786131171444
- Format Fachbuch
- Titel Anomaly Detection
- Herausgeber Betascript Publishing
- Anzahl Seiten 72
- Genre Informatik
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