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» Local and Global Complete Solution Learning Methods for QBF
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TCSV
2008
195views more  TCSV 2008»
13 years 3 months ago
Locality Versus Globality: Query-Driven Localized Linear Models for Facial Image Computing
Conventional subspace learning or recent feature extraction methods consider globality as the key criterion to design discriminative algorithms for image classification. We demonst...
Yun Fu, Zhu Li, Junsong Yuan, Ying Wu, Thomas S. H...
NIPS
2003
13 years 5 months ago
Learning with Local and Global Consistency
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to sem...
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal,...
CVPR
2012
IEEE
11 years 6 months ago
A learning based deformable template matching method for automatic rib centerline extraction and labeling in CT images
The automatic extraction and labeling of the rib centerlines is a useful yet challenging task in many clinical applications. In this paper, we propose a new approach integrating r...
Dijia Wu, David Liu, Zoltan Puskas, Chao Lu, Andre...
ICCV
2003
IEEE
14 years 5 months ago
Learning How to Inpaint from Global Image Statistics
Inpainting is the problem of filling-in holes in images. Considerable progress has been made by techniques that use the immediate boundary of the hole and some prior information o...
Anat Levin, Assaf Zomet, Yair Weiss
ML
2010
ACM
13 years 2 months ago
Semi-supervised local Fisher discriminant analysis for dimensionality reduction
When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled samples ...
Masashi Sugiyama, Tsuyoshi Idé, Shinichi Na...