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ISNN
2010
Springer
15 years 5 months ago
Particle Swarm Optimization Based Learning Method for Process Neural Networks
Abstract. This paper proposes a new learning method for process neural networks (PNNs) based on the Gaussian mixture functions and particle swarm optimization (PSO), called PSO-LM....
Kun Liu, Ying Tan, Xingui He
HYBRID
2010
Springer
16 years 1 months ago
On the connections between PCTL and dynamic programming
Probabilistic Computation Tree Logic (PCTL) is a wellknown modal logic which has become a standard for expressing temporal properties of finite-state Markov chains in the context...
Federico Ramponi, Debasish Chatterjee, Sean Summer...
FSTTCS
2006
Springer
15 years 10 months ago
Testing Probabilistic Equivalence Through Reinforcement Learning
We propose a new approach to verification of probabilistic processes for which the model may not be available. We use a technique from Reinforcement Learning to approximate how far...
Josee Desharnais, François Laviolette, Sami...
MA
2011
Springer
188views Communications» more  MA 2011»
15 years 1 months ago
A copula-based model of speculative price dynamics in discrete time
This paper suggests a new technique to construct first order Markov processes using products of copula functions, in the spirit of Darsow et al. (1992). The approach requires the...
Umberto Cherubini, Sabrina Mulinacci, Silvia Romag...
CVPR
2003
IEEE
16 years 8 months ago
Tracking Appearances with Occlusions
Occlusion is a difficult problem for appearance-based target tracking, especially when we need to track multiple targets simultaneously and maintain the target identities during t...
Ying Wu, Ting Yu, Gang Hua