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» Image Classification Using Marginalized Kernels for Graphs
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GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
13 years 11 months ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa
ICCV
2011
IEEE
12 years 7 months ago
Maximizing All Margins: Pushing Face Recognition with Kernel Plurality
We present two theses in this paper: First, performance of most existing face recognition algorithms improves if instead of the whole image, smaller patches are individually class...
Ritwik Kumar, Arunava Banerjee, CISE, Univ, Baba V...
ICML
2004
IEEE
14 years 8 months ago
Decentralized detection and classification using kernel methods
We consider the problem of decentralized detection under constraints on the number of bits that can be transmitted by each sensor. In contrast to most previous work, in which the ...
XuanLong Nguyen, Martin J. Wainwright, Michael I. ...
CVPR
2007
IEEE
14 years 9 months ago
Learning Kernel Expansions for Image Classification
Kernel machines (e.g. SVM, KLDA) have shown state-ofthe-art performance in several visual classification tasks. The classification performance of kernel machines greatly depends o...
Fernando De la Torre, Oriol Vinyals
ICPR
2010
IEEE
13 years 5 months ago
Multiple Kernel Learning with High Order Kernels
Previous Multiple Kernel Learning approaches (MKL) employ different kernels by their linear combination. Though some improvements have been achieved over methods using single kerne...
Shuhui Wang, Shuqiang Jiang, Qingming Huang, Qi Ti...