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» Learning the Structure of Dynamic Probabilistic Networks
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TSMC
2008
162views more  TSMC 2008»
13 years 7 months ago
Codevelopmental Learning Between Human and Humanoid Robot Using a Dynamic Neural-Network Model
The paper examines characteristics of interactive learning between human tutors and a robot having a dynamic neural network model which is inspired by human parietal cortex functio...
Jun Tani, Ryunosuke Nishimoto, Jun Namikawa, Masat...
CMSB
2009
Springer
14 years 2 months ago
Probabilistic Approximations of Signaling Pathway Dynamics
Systems of ordinary differential equations (ODEs) are often used to model the dynamics of complex biological pathways. We construct a discrete state model as a probabilistic appro...
Bing Liu, P. S. Thiagarajan, David Hsu
ICALT
2010
IEEE
13 years 7 months ago
A Social Network Analysis Perspective on Student Interaction within the Twitter Microblogging Environment
— This paper summarises the analyses of participant interaction within the Twitter microblogging environment. The study employs longitudinal probabilistic social network analysis...
Karen Stepanyan, Kerstin Borau, Carsten Ullrich
CEC
2009
IEEE
14 years 2 months ago
Structure learning and optimisation in a Markov-network based estimation of distribution algorithm
—Structure learning is a crucial component of a multivariate Estimation of Distribution algorithm. It is the part which determines the interactions between variables in the proba...
Alexander E. I. Brownlee, John A. W. McCall, Siddh...
FUIN
2008
108views more  FUIN 2008»
13 years 6 months ago
Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such rel...
Wannes Meert, Jan Struyf, Hendrik Blockeel