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310,094
Fast DempsterShafer clustering using a neural network structure
 Proceedings of the Seventh International Conference on Information Processing and Management of Uncertainty in Knowledgebased Systems (IPMU´'98)
, 1998
"... In this paper we study a problem within DempsterShafer theory where 2^n − 1 pieces of evidence are clustered by a neural structure into n clusters. The clustering is done by minimizing a metaconflict function. Previously we developed a method based on iterative optimization. However, for large scal ..."
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Cited by 12 (10 self)
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In this paper we study a problem within DempsterShafer theory where 2^n − 1 pieces of evidence are clustered by a neural structure into n clusters. The clustering is done by minimizing a metaconflict function. Previously we developed a method based on iterative optimization. However, for large
A neural network and iterative optimization hybrid for DempsterShafer clustering
 Proceedings of EuroFusion98 International Conference on Data Fusion (EF'98)
, 1998
"... In this paper we extend an earlier result within DempsterShafer theory ["Fast DempsterShafer Clustering Using a Neural Network Structure," in Proc. Seventh Int. Conf. Information Processing and Management of Uncertainty in KnowledgeBased Systems (IPMU'98)] where a large number of p ..."
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Cited by 5 (5 self)
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In this paper we extend an earlier result within DempsterShafer theory ["Fast DempsterShafer Clustering Using a Neural Network Structure," in Proc. Seventh Int. Conf. Information Processing and Management of Uncertainty in KnowledgeBased Systems (IPMU'98)] where a large number
Simultaneous DempsterShafer clustering and gradual determination of number of clusters using a neural network structure
 Proceedings of the 1999 Information, Decision and Control Conference (IDC'99)
, 1999
"... In this paper we extend an earlier result within DempsterShafer theory ["Fast DempsterShafer Clustering Using a Neural Network Structure," in Proc. Seventh Int. Conf. Information Processing and Management of Uncertainty in KnowledgeBased Systems (IPMU’98)] where several pieces of eviden ..."
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Cited by 7 (7 self)
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In this paper we extend an earlier result within DempsterShafer theory ["Fast DempsterShafer Clustering Using a Neural Network Structure," in Proc. Seventh Int. Conf. Information Processing and Management of Uncertainty in KnowledgeBased Systems (IPMU’98)] where several pieces
Combination of evidence in DempsterShafer theory
, 2002
"... DempsterShafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. DempsterShafer theory does not require a ..."
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Cited by 79 (2 self)
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expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for DempsterShafer structures and provides examples of the implementation
The DempsterShafer calculus for statisticians
 International Journal of Approximate Reasoning
, 2007
"... The DempsterShafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p, q, r) where p is the probability “for ” the assertion, q is the probability “against” the assertion, and r is the probability of “ ..."
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Cited by 46 (1 self)
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The DempsterShafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p, q, r) where p is the probability “for ” the assertion, q is the probability “against” the assertion, and r is the probability
Evolving Neural Networks through Augmenting Topologies
 Evolutionary Computation
"... An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixedtopology method on a challenging benchmark reinforcement learning task ..."
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Cited by 524 (113 self)
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An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixedtopology method on a challenging benchmark reinforcement learning
Adaptive clustering for mobile wireless networks
 IEEE Journal on Selected Areas in Communications
, 1997
"... This paper describes a selforganizing, multihop, mobile radio network, which relies on a code division access scheme for multimedia support. In the proposed network architecture, nodes are organized into nonoverlapping clusters. The clusters are independently controlled and are dynamically reconfig ..."
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Cited by 556 (11 self)
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This paper describes a selforganizing, multihop, mobile radio network, which relies on a code division access scheme for multimedia support. In the proposed network architecture, nodes are organized into nonoverlapping clusters. The clusters are independently controlled and are dynamically
Finding community structure in networks using the eigenvectors of matrices
, 2006
"... We consider the problem of detecting communities or modules in networks, groups of vertices with a higherthanaverage density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity ” over possible div ..."
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Cited by 500 (0 self)
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number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a new centrality measure that identifies those vertices that occupy central positions within the communities to which they belong
A neural network classifier based on DempsterShafer theory
, 1997
"... A new adaptive pattern classifier based on the DempsterShafer theory of evidence is presented. This method uses reference patterns as items of evidence regarding the class membership of each input pattern under consideration. This evidence is represented by basic belief assignments (BBAs) and poole ..."
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Cited by 27 (1 self)
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A new adaptive pattern classifier based on the DempsterShafer theory of evidence is presented. This method uses reference patterns as items of evidence regarding the class membership of each input pattern under consideration. This evidence is represented by basic belief assignments (BBAs
Results 1  10
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310,094