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PRL
2006
106views more  PRL 2006»
14 years 9 months ago
Invariances in kernel methods: From samples to objects
This paper presents a general method for incorporating prior knowledge into kernel methods such as Support Vector Machines. It applies when the prior knowledge can be formalized b...
Alexei Pozdnoukhov, Samy Bengio
PKDD
2009
Springer
113views Data Mining» more  PKDD 2009»
15 years 4 months ago
Feature Selection for Density Level-Sets
A frequent problem in density level-set estimation is the choice of the right features that give rise to compact and concise representations of the observed data. We present an eï¬...
Marius Kloft, Shinichi Nakajima, Ulf Brefeld
AVBPA
2005
Springer
226views Biometrics» more  AVBPA 2005»
15 years 3 months ago
Discriminant Analysis Based on Kernelized Decision Boundary for Face Recognition
A novel nonlinear discriminant analysis method, Kernelized Decision Boundary Analysis (KDBA), is proposed in our paper, whose Decision Boundary feature vectors are the normal vecto...
Baochang Zhang, Xilin Chen, Wen Gao
NIPS
2001
14 years 11 months ago
A Sequence Kernel and its Application to Speaker Recognition
A novel approach for comparing sequences of observations using an explicit-expansion kernel is demonstrated. The kernel is derived using the assumption of the independence of the ...
W. M. Campbell
CVPR
2005
IEEE
15 years 3 months ago
Nonlinear Face Recognition Based on Maximum Average Margin Criterion
This paper proposes a novel nonlinear discriminant analysis method named by Kernerlized Maximum Average Margin Criterion (KMAMC), which has combined the idea of Support Vector Mac...
Baochang Zhang, Xilin Chen, Shiguang Shan, Wen Gao