110 search results - page 3 / 22 » Optimal kernel selection in Kernel Fisher discriminant analy... |
CVPR
2010
IEEE
13 years 12 months ago
2010
IEEE
Kernel methods provide an efficient mechanism to derive nonlinear algorithms. In classification problems as well as in feature extraction, kernel-based approaches map the original...
AVBPA
2005
Springer
14 years 1 months ago
2005
Springer
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...
ECCV
2006
Springer
14 years 9 months ago
2006
Springer
Many linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD) methods are based on the restrictive assumption that the data are homoscedastic. In this paper...
ICDM
2009
IEEE
14 years 2 months ago
2009
IEEE
—We consider the problem of learning a linear combination of pre-specified kernel matrices in the Fisher discriminant analysis setting. Existing methods for such a task impose a...
NIPS
2007
13 years 9 months ago
2007
We propose to investigate test statistics for testing homogeneity based on kernel Fisher discriminant analysis. Asymptotic null distributions under null hypothesis are derived, an...