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KDD
2009
ACM
215views Data Mining» more  KDD 2009»
14 years 11 months ago
Large-scale sparse logistic regression
Logistic Regression is a well-known classification method that has been used widely in many applications of data mining, machine learning, computer vision, and bioinformatics. Spa...
Jun Liu, Jianhui Chen, Jieping Ye
CORR
2010
Springer
167views Education» more  CORR 2010»
13 years 11 months ago
Network Flow Algorithms for Structured Sparsity
We consider a class of learning problems that involve a structured sparsityinducing norm defined as the sum of -norms over groups of variables. Whereas a lot of effort has been pu...
Julien Mairal, Rodolphe Jenatton, Guillaume Obozin...
ECCV
2008
Springer
15 years 22 days ago
Learning Optical Flow
Assumptions of brightness constancy and spatial smoothness underlie most optical flow estimation methods. In contrast to standard heuristic formulations, we learn a statistical mod...
Deqing Sun, Stefan Roth, J. P. Lewis, Michael J. B...
ESANN
2004
14 years 8 days ago
Sparse LS-SVMs using additive regularization with a penalized validation criterion
This paper is based on a new way for determining the regularization trade-off in least squares support vector machines (LS-SVMs) via a mechanism of additive regularization which ha...
Kristiaan Pelckmans, Johan A. K. Suykens, Bart De ...
MM
2010
ACM
177views Multimedia» more  MM 2010»
13 years 11 months ago
Image tag refinement towards low-rank, content-tag prior and error sparsity
The vast user-provided image tags on the popular photo sharing websites may greatly facilitate image retrieval and management. However, these tags are often imprecise and/or incom...
Guangyu Zhu, Shuicheng Yan, Yi Ma