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» Smooth Optimization for Effective Multiple Kernel Learning
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ICML
2004
IEEE
15 years 10 months ago
Multiple kernel learning, conic duality, and the SMO algorithm
While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. ...
Francis R. Bach, Gert R. G. Lanckriet, Michael I. ...
ICASSP
2008
IEEE
15 years 4 months ago
Multiple kernel learning for speaker verification
Many speaker verification (SV) systems combine multiple classifiers using score-fusion to improve system performance. For SVM classifiers, an alternative strategy is to combine...
Chris Longworth, Mark J. F. Gales
88
Voted
ICCV
2009
IEEE
14 years 7 months ago
Group-sensitive multiple kernel learning for object categorization
In this paper, we propose a group-sensitive multiple kernel learning (GS-MKL) method to accommodate the intra-class diversity and the inter-class correlation for object categoriza...
Jingjing Yang, Yuanning Li, YongHong Tian, Lingyu ...
PKDD
2009
Springer
138views Data Mining» more  PKDD 2009»
15 years 4 months ago
Margin and Radius Based Multiple Kernel Learning
A serious drawback of kernel methods, and Support Vector Machines (SVM) in particular, is the difficulty in choosing a suitable kernel function for a given dataset. One of the appr...
Huyen Do, Alexandros Kalousis, Adam Woznica, Melan...
ICDM
2006
IEEE
225views Data Mining» more  ICDM 2006»
15 years 3 months ago
Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples ar...
Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen