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ICONIP
2008
14 years 11 months ago
The Diversity of Regression Ensembles Combining Bagging and Random Subspace Method
Abstract. The concept of Ensemble Learning has been shown to increase predictive power over single base learners. Given the bias-variancecovariance decomposition, diversity is char...
Alexandra Scherbart, Tim W. Nattkemper
TCSV
2008
195views more  TCSV 2008»
14 years 9 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...
ICMCS
2005
IEEE
103views Multimedia» more  ICMCS 2005»
15 years 3 months ago
Neighborhood issue in single-frame image super-resolution
Super-Resolution is the problem of generating one or a set of high-resolution images from one or a sequence of lowresolution frames. Most methods have been proposed for super-reso...
K. Su, Qi Tian, Qing Xue, Nicu Sebe, Jingsheng Ma
TKDE
2008
195views more  TKDE 2008»
14 years 9 months ago
Learning a Maximum Margin Subspace for Image Retrieval
One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. To narrow down this gap...
Xiaofei He, Deng Cai, Jiawei Han
TIP
2010
188views more  TIP 2010»
14 years 7 months ago
On-line Learning of Mutually Orthogonal Subspaces for Face Recognition by Image Sets
—We address the problem of face recognition by matching image sets. Each set of face images is represented by a subspace (or linear manifold) and recognition is carried out by su...
Tae-Kyun Kim, Josef Kittler, Roberto Cipolla