613 search results - page 40 / 123 » Learning the Structure of Linear Latent Variable Models |
SDM
2009
SIAM
14 years 4 months ago
2009
SIAM
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...
AAAI
2008
13 years 10 months ago
2008
Research has shown promise in the design of large scale common sense probabilistic models to infer human state from environmental sensor data. These models have made use of mined ...
AAAI
2007
13 years 10 months ago
2007
Sparsity-promoting L1-regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the ...
NIPS
1997
13 years 9 months ago
1997
We address the problem of learning structure in nonlinear Markov networks with continuous variables. This can be viewed as non-Gaussian multidimensional density estimation exploit...
NIPS
2007
13 years 9 months ago
2007
Hierarchical penalization is a generic framework for incorporating prior information in the fitting of statistical models, when the explicative variables are organized in a hiera...