Idiotypic network models give one possible justification for the appearance of tolerance for a certain category of cells while maintaining immunization for the others. In this paper, we provide new evidence that the m...
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Idiotypic network models give one possible justification for the appearance of tolerance for a certain category of cells while maintaining immunization for the others. In this paper, we provide new evidence that the manner in which affinity is defined in an idiotypic network model imposes a definite topology on the connectivity of the potential idiotypic network that can emerge. The resulting topology is responsible for very different qualitative behaviour of the network. We show that using a 2D shape-space model with affinity based on complementary regions, a cluster-free topology results that clearly divides the space into distinct zones;if antigens fall into a zone in which there are no available antibodies to bind to, they are tolerated. On the other hand, if they fall into a zone in which there are highly concentrated antibodies available for binding, then they will be eliminated. On the contrary, using a 2D shape space with an affinity function based on cell similarity, a highly clustered topology emerges in which there is no separation of the space into isolated tolerant and non-tolerant zones. Using a bit-string shape space, both similar and complementary affinity measures also result in highly clustered networks. In the networks whose topologies exhibit high clustering, the tolerant and intolerant zones are so intertwined that the networks either reject all antigen or tolerate all antigen. We show that the distribution and topology of the antibody network defined by the complete set of nodes and links-an autonomous feature of the system-therefore selects which antigens are tolerated and which are eliminated. (c) 2007 Elsevier Ltd. All rights reserved.
In this paper, we focus on the potential for applying Kernel Methods into Artificial Immune Systems. This is based on the fact that;the commonly employed "affinity functions" car. usually be replaced by kern...
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ISBN:
(纸本)9783540850717
In this paper, we focus on the potential for applying Kernel Methods into Artificial Immune Systems. This is based on the fact that;the commonly employed "affinity functions" car. usually be replaced by kernel functions, leading to algorithms operating in the feature space. A discussion of this applicability in negative/positive selection algorithms, the dendritic cell algorithm and immune network algorithms is Conducted. As a practical application. we modify the aiNet (Artificial Immune Network) algorithm to use a kernel function, and analyze its compression quality using synthetic datasets. It is concluded that the use of properly adjusted kernel functions can improve the compression quality of the algorithm. Furthermore, we briefly discuss sortie of the future implications of using kernel functions in in immune-inspired algorithms.
The equivalence of affinities in fuzzy connectedness (FC) is a novel concept which gives us the ability to study affinity functions and their precise connection with FC algorithms. Two seminal papers by Ciesielski and...
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The equivalence of affinities in fuzzy connectedness (FC) is a novel concept which gives us the ability to study affinity functions and their precise connection with FC algorithms. Two seminal papers by Ciesielski and Udupa create a strong theoretical background and provide some useful practical examples. Our intention here is to investigate this concept further because from a practical viewpoint if we are able to determine the equivalence classes for a given set of affinity functions and narrow it down to a much smaller set of nonequivalent affinities, then the set can be used more effectively in an optimization framework which searches for the best affinity function or parameters for a special task. In other words, we can find the best configuration for a set of given hardware or an image set with special characteristics. From a theoretical perspective, we are interested in the complexity of this problem, i.e. determining equivalence classes. Here, an affinity operator is used which is a function of a given parameter and maps different parameter values for different affinity functions. Our first questions, namely how many different meaningful, non-equivalent affinities there are and how we can enumerate them, led us to a general problem of how the equivalent affinities partition the parameter's domain and how the corresponding equivalence classes can be determined. We will provide a general algorithm schema to construct special algorithms which are able to compute the equivalence classes. We will also analyze a special but very common scenario of when the affinity operator combines two affinities (e.g. a homogeneity and an object feature-based affinity) using an aggregation operator (e.g. weighted average) and the particular parameter defines the weights of the affinities. Based on the general algorithm schema, we propose algorithms for this special case and we determine their complexity as well. These algorithms will be tested on two sets of medical images, namel
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