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» Kernel PLS-SVC for Linear and Nonlinear Classification
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NECO
2000
190views more  NECO 2000»
12 years 1 months ago
Generalized Discriminant Analysis Using a Kernel Approach
We present a new method that we call Generalized Discriminant Analysis (GDA) to deal with nonlinear discriminant analysis using kernel function operator. The underlying theory is ...
G. Baudat, Fatiha Anouar
FGR
2006
IEEE
255views Biometrics» more  FGR 2006»
12 years 5 months ago
Incremental Kernel SVD for Face Recognition with Image Sets
Non-linear subspaces derived using kernel methods have been found to be superior compared to linear subspaces in modeling or classification tasks of several visual phenomena. Such...
Tat-Jun Chin, Konrad Schindler, David Suter
PAMI
2002
148views more  PAMI 2002»
12 years 1 months ago
Two Variations on Fisher's Linear Discriminant for Pattern Recognition
Discriminants are often used in pattern recognition to separate clusters of points in some multidimensional "feature" space. This paper provides two fast and simple techn...
Tristrom Cooke
ICPR
2006
IEEE
13 years 3 months ago
Dimensionality Reduction with Adaptive Kernels
1 A kernel determines the inductive bias of a learning algorithm on a specific data set, and it is beneficial to design specific kernel for a given data set. In this work, we propo...
Shuicheng Yan, Xiaoou Tang
ISNN
2007
Springer
12 years 8 months ago
Extensions of Manifold Learning Algorithms in Kernel Feature Space
Manifold learning algorithms have been proven to be capable of discovering some nonlinear structures. However, it is hard for them to extend to test set directly. In this paper, a ...
Yaoliang Yu, Peng Guan, Liming Zhang
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