Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Case Memory and Retrieval Based on the Immune System
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Properties of the bersini experiment on self-assertion
Proceedings of the 8th 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
Structure versus function: a topological perspective on immune networks
Natural Computing: an international journal
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General properties of distance functions and of affinity functions are discussed in this paper. Reasons are given why a distance function for (n based shape-spaces should be a metric. Several distance functions that are used in shape-spaces are examined and it is shown that not all of them are metrics. It is shown which impact the type of the distance function has on the shape-space, in particular on the form of recognition or affinity regions in the shape-space. Affinity functions should be defined in such a way that they determine an affinity region with positive values inside that region and zero or negative values outside. The form of an affinity function depends on the type of the underlying distance function. This is demonstrated with several examples.