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» A theoretical framework for multiple neural network systems
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GECCO
2009
Springer
188views Optimization» more  GECCO 2009»
14 years 22 days ago
Exploiting multiple classifier types with active learning
Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...
Zhenyu Lu, Josh Bongard
TWC
2008
235views more  TWC 2008»
13 years 8 months ago
Optimal spectrum sensing framework for cognitive radio networks
Spectrum sensing is the key enabling technology for cognitive radio networks. The main objective of spectrum sensing is to provide more spectrum access opportunities to cognitive r...
Won-Yeol Lee, Ian F. Akyildiz
NN
2006
Springer
13 years 8 months ago
Propagation and control of stochastic signals through universal learning networks
The way of propagating and control of stochastic signals through Universal Learning Networks (ULNs) and its applications are proposed. ULNs have been already developed to form a s...
Kotaro Hirasawa, Shingo Mabu, Jinglu Hu
FOCS
2009
IEEE
14 years 3 months ago
Approximability of Combinatorial Problems with Multi-agent Submodular Cost Functions
Abstract— Applications in complex systems such as the Internet have spawned recent interest in studying situations involving multiple agents with their individual cost or utility...
Gagan Goel, Chinmay Karande, Pushkar Tripathi, Lei...
ENTCS
2007
111views more  ENTCS 2007»
13 years 8 months ago
Multi Labelled Transition Systems: A Semantic Framework for Nominal Calculi
Action Labelled transition systems (LTS) have proved to be a fundamental model for describing and proving properties of concurrent systems. In this paper,Multiple Labelled Transit...
Rocco De Nicola, Michele Loreti