Self-Organizing Neural Nets for Automatic Anomaly Identification

Self-Organizing Neural Nets for Automatic Anomaly Identification

Self-organizing maps are a type of unsupervised neural network which fit themselves to the pattern of information in multi-dimensional data in an orderly fashion. The curvature and harvesting of the classification with low probability in a SOM are an indicator of multi-attribute anomalies for further investigation.

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