259 search results - page 2 / 52 » A Hybrid Convergent Method for Learning Probabilistic Networ... |
HIS
2003
13 years 6 months ago
2003
– Probabilistic Inference Networks are becoming increasingly popular for modeling and reasoning in uncertain domains. In the past few years, many efforts have been made in learni...
APIN
1999
13 years 4 months ago
1999
We present a method for mapping a given Bayesian network to a Boltzmann machine architecture, in the sense that the the updating process of the resulting Boltzmann machine model pr...
CDC
2010
IEEE
12 years 12 months ago
2010
IEEE
The asymptotic behavior of stochastic gradient algorithms is studied. Relying on some results of differential geometry (Lojasiewicz gradient inequality), the almost sure pointconve...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
Although each iteration of the popular kMeans clustering heuristic scales well to larger problem sizes, it often requires an unacceptably-high number of iterations to converge to ...
GECCO
2008
Springer
13 years 6 months ago
2008
Springer
Probabilistic model building methods can render difficult problems feasible by identifying and exploiting dependencies. They build a probabilistic model from the statistical prope...