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» Kernel PLS-SVC for Linear and Nonlinear Classification
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TNN
2008
81views more  TNN 2008»
11 years 11 months ago
Nonlinear Knowledge-Based Classification
Prior knowledge over general nonlinear sets is incorporated into nonlinear kernel classification problems as linear constraints in a linear program. The key tool in this incorpora...
Olvi L. Mangasarian, Edward W. Wild
ICML
2003
IEEE
13 years 22 days ago
Kernel PLS-SVC for Linear and Nonlinear Classification
A new method for classification is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by a ...
Roman Rosipal, Leonard J. Trejo, Bryan Matthews
ICIP
2005
IEEE
13 years 1 months ago
Nonlinear dimensionality reduction for classification using kernel weighted subspace method
We study the use of kernel subspace methods that learn low-dimensional subspace representations for classification tasks. In particular, we propose a new method called kernel weigh...
Guang Dai, Dit-Yan Yeung
CVPR
2010
IEEE
12 years 4 months ago
Bayes Optimal Kernel Discriminant Analysis
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...
Di You, Aleix Martinez
CVPR
2009
IEEE
1976views Computer Vision» more  CVPR 2009»
13 years 7 months ago
Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification
Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n...
Jianchao Yang, Kai Yu, Yihong Gong, Thomas S. Huan...
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