613 search results - page 53 / 123 » Learning the Structure of Linear Latent Variable Models |
UAI
1998
13 years 10 months ago
1998
In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a beli...
ICDM
2003
IEEE
14 years 2 months ago
2003
IEEE
Learning Bayesian network structure from large-scale data sets, without any expertspecified ordering of variables, remains a difficult problem. We propose systematic improvements ...
AUTOMATICA
2006
13 years 9 months ago
2006
This paper investigates new ways of inferring nonlinear dependence from measured data. The existence of unique linear and nonlinear sub-spaces which are structural invariants of g...
SIMPRA
2008
13 years 8 months ago
2008
The Wiener model is a versatile nonlinear block oriented model structure for miscellaneous applications. In this paper a method for identifying the parameters of such a model usin...
ICML
2005
IEEE
14 years 9 months ago
2005
IEEE
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., li...