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» An Artificial Chemistry for Networking
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CEC
2003
IEEE
15 years 11 months ago
Comparing neural networks and Kriging for fitness approximation in evolutionary optimization
Neural networks and the Kriging method are compared for constructing £tness approximation models in evolutionary optimization algorithms. The two models are applied in an identica...
Lars Willmes, Thomas Bäck, Yaochu Jin, Bernha...
EVOW
2008
Springer
15 years 8 months ago
Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control
In many cases what matters is not whether a false discovery is made or not but the expected proportion of false discoveries among all the discoveries made, i.e. the so-called false...
Jose M. Peña
FLAIRS
2001
15 years 7 months ago
Maximal Prime Subgraph Decomposition of Bayesian Networks
A maximal prime subgraph decomposition junction tree (MPD-JT) is a useful computational structure that facilitates lazy propagation in Bayesian networks (BNs). A graphical method ...
Kristian G. Olesen, Anders L. Madsen
TWC
2010
15 years 1 months ago
Efficient Dynamic Spectrum Sharing in Cognitive Radio Networks: Centralized Dynamic Spectrum Leasing (C-DSL)
In this paper, we propose the concept of centralized dynamic spectrum leasing (C-DSL), in which multiple primary users belonging to the same primary system participate in the spect...
K. Hakim, Sudharman K. Jayaweera, Georges El-Howay...
IJCNN
2006
IEEE
16 years 12 days ago
Fast Modifications of the SpikeProp Algorithm
Abstract - In this paper we develop and analyze Spiking Neural Network (SNN) versions of Resilient Propagation (RProp) and QuickProp, both training methods used to speed up trainin...
Sam McKennoch, Dingding Liu, Linda G. Bushnell