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148
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JMLR
2010
129views more  JMLR 2010»
14 years 10 months ago
Expectation Truncation and the Benefits of Preselection In Training Generative Models
We show how a preselection of hidden variables can be used to efficiently train generative models with binary hidden variables. The approach is based on Expectation Maximization (...
Jörg Lücke, Julian Eggert
112
Voted
ICASSP
2011
IEEE
14 years 7 months ago
How efficient is estimation with missing data?
In this paper, we present a new evaluation approach for missing data techniques (MDTs) where the efficiency of those are investigated using listwise deletion method as reference....
Seliz G. Karadogan, Letizia Marchegiani, Lars Kai ...
164
Voted
SDM
2011
SIAM
233views Data Mining» more  SDM 2011»
14 years 6 months ago
Multi-Instance Mixture Models
Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vecto...
James R. Foulds, Padhraic Smyth
134
Voted
CVPR
2008
IEEE
16 years 5 months ago
Sequential sparsification for change detection
This paper presents a general method for segmenting a vector valued sequence into an unknown number of subsequences where all data points from a subsequence can be represented wit...
Necmiye Ozay, Mario Sznaier, Octavia I. Camps
118
Voted
ICCV
2003
IEEE
16 years 5 months ago
Towards Gauge Invariant Bundle Adjustment: A Solution Based on Gauge Dependent Damping
Bundle ajustment is used to obtain accurate visual reconstructions by minimizing the reprojection error. The coordinate frame ambiguity, or more generality the gauge freedoms, has...
Adrien Bartoli