Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
An Immunological Approach to Change Detection: Theoretical Results
CSFW '96 Proceedings of the 9th IEEE workshop on Computer Security Foundations
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
One-class svms for document classification
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
Is negative selection appropriate for anomaly detection?
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Discriminating self from non-self with finite mixtures of multivariate Bernoulli distributions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A hybrid AIS-SVM ensemble approach for text classification
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
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A statistical model is presented as an alternative to negative selection in anomaly detection of discrete data. We extend the use of probabilistic generative models from fixed-length binary strings into variable-length strings from a finite symbol alphabet using a mixture model of multinomial distributions for the frequency of adjacent symbols in a sliding window over a string. Robust and localized change analysis of text corpora is viewed as an application area.