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ICML
2005
IEEE
14 years 5 months ago
New d-separation identification results for learning continuous latent variable models
Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hid...
Ricardo Silva, Richard Scheines
ICML
2009
IEEE
14 years 5 months ago
Learning structural SVMs with latent variables
We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. Our proposal covers a large range of application prob...
Chun-Nam John Yu, Thorsten Joachims
ICDE
2005
IEEE
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14 years 6 months ago
Online Latent Variable Detection in Sensor Networks
Sensor networks attract increasing interest, for a broad range of applications. Given a sensor network, one key issue becomes how to utilize it efficiently and effectively. In par...
Jimeng Sun, Spiros Papadimitriou, Christos Falouts...
CVPR
2007
IEEE
14 years 6 months ago
Kernel-based Tracking from a Probabilistic Viewpoint
In this paper, we present a probabilistic formulation of kernel-based tracking methods based upon maximum likelihood estimation. To this end, we view the coordinates for the pixel...
Quang Anh Nguyen, Antonio Robles-Kelly, Chunhua Sh...