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» Feature selection in a kernel space
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132
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MCS
2010
Springer
15 years 5 months ago
Tomographic Considerations in Ensemble Bias/Variance Decomposition
Abstract. Classifier decision fusion has been shown to act in a manner analogous to the back-projection of Radon transformations when individual classifier feature sets are non o...
David Windridge
117
Voted
CVPR
2010
IEEE
16 years 2 days ago
Connecting Modalities: Semi-supervised Segmentation and Annotation of Images Using Unaligned Text Corpora
We propose a semi-supervised model which segments and annotates images using very few labeled images and a large unaligned text corpus to relate image regions to text labels. Give...
Richard Socher, Li Fei-Fei
159
Voted
TNN
2008
182views more  TNN 2008»
15 years 3 months ago
Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines
Abstract--Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) ...
Ivor Wai-Hung Tsang, András Kocsor, James T...
119
Voted
ESANN
2008
15 years 5 months ago
Comparison of sparse least squares support vector regressors trained in primal and dual
In our previous work, we have developed sparse least squares support vector regressors (sparse LS SVRs) trained in the primal form in the reduced empirical feature space. In this p...
Shigeo Abe
153
Voted
PRL
2010
209views more  PRL 2010»
14 years 10 months ago
Efficient update of the covariance matrix inverse in iterated linear discriminant analysis
For fast classification under real-time constraints, as required in many imagebased pattern recognition applications, linear discriminant functions are a good choice. Linear discr...
Jan Salmen, Marc Schlipsing, Christian Igel