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» On the Convergence of Boosting Procedures
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CSDA
2006
90views more  CSDA 2006»
13 years 8 months ago
Estimation in covariate-adjusted regression
Abstract: The method of covariate adjusted regression was recently proposed for situations where both predictors and response in a regression model are not directly observed, but a...
Damla Sentürk, Danh V. Nguyen
DATAMINE
2006
130views more  DATAMINE 2006»
13 years 8 months ago
Mining Adaptive Ratio Rules from Distributed Data Sources
Different from traditional association-rule mining, a new paradigm called Ratio Rule (RR) was proposed recently. Ratio rules are aimed at capturing the quantitative association kno...
Jun Yan, Ning Liu, Qiang Yang, Benyu Zhang, QianSh...
CORR
2007
Springer
130views Education» more  CORR 2007»
13 years 8 months ago
Lagrangian Relaxation for MAP Estimation in Graphical Models
Abstract— We develop a general framework for MAP estimation in discrete and Gaussian graphical models using Lagrangian relaxation techniques. The key idea is to reformulate an in...
Jason K. Johnson, Dmitry M. Malioutov, Alan S. Wil...
MP
2002
176views more  MP 2002»
13 years 8 months ago
UOBYQA: unconstrained optimization by quadratic approximation
UOBYQA is a new algorithm for general unconstrained optimization calculations, that takes account of the curvature of the objective function, F say, by forming quadratic models by ...
M. J. D. Powell
CVPR
2000
IEEE
14 years 10 months ago
A General Method for Errors-in-Variables Problems in Computer Vision
The Errors-in-Variables (EIV) model from statistics is often employed in computer vision thoughonlyrarely under this name. In an EIV model all the measurements are corrupted by no...
Bogdan Matei, Peter Meer