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» Robust bounds for classification via selective sampling
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JMLR
2002
89views more  JMLR 2002»
13 years 7 months ago
A Robust Minimax Approach to Classification
When constructing a classifier, the probability of correct classification of future data points should be maximized. We consider a binary classification problem where the mean and...
Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib...
ICPR
2010
IEEE
13 years 5 months ago
Variational Mixture of Experts for Classification with Applications to Landmine Detection
Abstract--In this paper, we (1) provide a complete framework for classification using Variational Mixture of Experts (VME); (2) derive the variational lower bound; and (3) apply th...
Seniha Esen Yuksel, Paul D. Gader
TCSV
2008
195views more  TCSV 2008»
13 years 7 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...

Publication
468views
13 years 4 months ago
Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR)
When appearance variation of object and its background, partial occlusion or deterioration in object images occurs, most existing visual tracking methods tend to fail in tracking ...
Zhenjun Han, Jianbin Jiao, Baochang Zhang, Qixiang...
CVPR
2005
IEEE
14 years 1 months ago
Online Selecting Discriminative Tracking Features Using Particle Filter
The paper proposes a method to keep the tracker robust to background clutters by online selecting discriminative features from a large feature space. Furthermore, the feature sele...
Jianyu Wang, Xilin Chen, Wen Gao