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GECCO
2007
Springer
558views Optimization» more  GECCO 2007»
14 years 5 months ago
A chain-model genetic algorithm for Bayesian network structure learning
Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....
Ratiba Kabli, Frank Herrmann, John McCall
NIPS
2003
14 years 7 days ago
Learning Bounds for a Generalized Family of Bayesian Posterior Distributions
In this paper we obtain convergence bounds for the concentration of Bayesian posterior distributions (around the true distribution) using a novel method that simplifies and enhan...
Tong Zhang
ICIP
2000
IEEE
15 years 14 days ago
Motion Estimation Using Adaptive Blocksize Observation Model and Efficient Multiscale Regularization
Bayesian motion estimation requires two pdf models: observation model and motion field (prior) model. The optimization process for this method uses sequential approach, e.g. simul...
Stephanus Suryadarma Tandjung, Teddy Surya Gunawan...
TVCG
2008
100views more  TVCG 2008»
13 years 10 months ago
Two-Character Motion Analysis and Synthesis
In this paper, we deal with the problem of synthesizing novel motions of standing-up martial arts such as Kickboxing, Karate, and Taekwondo performed by a pair of humanlike charact...
Taesoo Kwon, Young-Sang Cho, Sang Il Park, Sung Yo...
IFIP12
2008
14 years 10 days ago
Bayesian Networks Optimization Based on Induction Learning Techniques
Obtaining a bayesian network from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper, we define an automatic learni...
Paola Britos, Pablo Felgaer, Ramón Garc&iac...