Novelty detection is the identification of new or unknown data or signals that a machine learning system is not aware of during training[1]. Novelty detection is one-class classification.
Novelty Detection: Domain General and Domain Specific Mechanisms
[ , Cognitive Neuroscience, Computational Modeling ] ...
The applications of neural networks are almost limitless but they fall into several main categories like classification, modeling, forecasting and novelty detection.
There are important classes of tasks, such as novelty detection (e.g. fraud detection), for which quantified variance is essential.
A second possible use is in novelty detection. SOFM networks can learn to recognize clusters in the training data, and respond to it. If new data, unlike previous cases, is encountered, the network fails to recognize it and this indicates novelty.
networks are applied tend to fall within the following broad categories of Function approximation, or regression analysis, including time series prediction and modeling, classification, including pattern and sequence recognition, novelty detection ...
 See also: Classification, Neural network, Knowledge, Pattern recognition, Artificial intelligence
  
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