A Generative Model for Self/Non-self Discrimination in Strings
نویسنده
چکیده
A statistical generative model is presented as an alternative to negative selection in anomaly detection of string data. We extend the probabilistic approach to binary classification from fixed-length binary strings into variable-length strings from a finite symbol alphabet by fitting a mixture model of multinomial distributions for the frequency of adjacent symbols. Robust and localized change analysis of text documents is viewed as an application area.
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