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JMLR
2010
202views more  JMLR 2010»
14 years 10 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
158
Voted
ECCV
2006
Springer
16 years 5 months ago
Perspective n-View Multibody Structure-and-Motion Through Model Selection
Multi-body structure-and-motion (MSaM) is the problem to establish the multiple-view geometry of an image sequence of a 3D scene, where the scene consists of multiple rigid objects...
Konrad Schindler, James U, Hanzi Wang
144
Voted
ALT
2003
Springer
16 years 13 days ago
Kernel Trick Embedded Gaussian Mixture Model
In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter ...
Jingdong Wang, Jianguo Lee, Changshui Zhang
COLT
2000
Springer
15 years 7 months ago
Model Selection and Error Estimation
We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalizatio...
Peter L. Bartlett, Stéphane Boucheron, G&aa...
146
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IDA
2010
Springer
15 years 7 months ago
Statistical Modelling for Data from Experiments with Short Hairpin RNAs
This paper delivers an example of applying intelligent data analysis to biological data where the success of the project was only possible due to joint efforts of the experts from ...
Frank Klawonn, Torsten Wüstefeld, Lars Zender