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This paper presents an immune-based approach to recognition of handwritten words, or more generally, to binary classification. It is inspired by the negative selection mechanism attending the discrimination between self and nonself molecules in living organisms. Our approach incorporates two types of detectors: binary and real-valued. Relatively short binary detectors are used for primary detection,...
One of the key parameters of algorithms for anomaly detection is the metric (norm) applied to calculate the distance between every two samples which reflect its proximity. It is especially important when we operate on real-valued high dimensional datasets, i.e. when we deal with the problem of intruders detection in computer networks. As observed, the most popular Euclidean norm becomes meaningless...
This paper presents a new dual model combining binary and real-valued representations of samples for negative selection algorithms. Recent research show that the two types of encoding can produce quite good results for some types of datasets when they are applied separately in such algorithms. Besides a number of efficient algorithms, various affinity (or similarity) functions fitted to particular...
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