275 search results - page 13 / 55 » A geometric view on learning Bayesian network structures |
ICML
2007
IEEE
15 years 10 months ago
2007
IEEE
We propose a class of graphical models appropriate for structure prediction problems where the model structure is a function of the output structure. Incremental Sigmoid Belief Ne...
PERCOM
2007
ACM
15 years 9 months ago
2007
ACM
Abstract. A major challenge in pervasive computing is to learn activity patterns, such as bathing and cleaning from sensor data. Typical sensor deployments generate sparse datasets...
ICML
2010
IEEE
14 years 8 months ago
2010
IEEE
Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with heterogeneity and non-stationarity in temporal processes. Various ap...
IJAR
2010
14 years 8 months ago
2010
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...
NIPS
2008
14 years 11 months ago
2008
With the increased availability of data for complex domains, it is desirable to learn Bayesian network structures that are sufficiently expressive for generalization while at the ...