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ICIC
2005
Springer
14 years 2 months ago
Methods of Decreasing the Number of Support Vectors via k-Mean Clustering
This paper proposes two methods which take advantage of k -mean clustering algorithm to decrease the number of support vectors (SVs) for the training of support vector machine (SVM...
Xiao-Lei Xia, Michael R. Lyu, Tat-Ming Lok, Guang-...
CDC
2009
IEEE
180views Control Systems» more  CDC 2009»
13 years 12 months ago
Robustness analysis for Least Squares kernel based regression: an optimization approach
—In kernel based regression techniques (such as Support Vector Machines or Least Squares Support Vector Machines) it is hard to analyze the influence of perturbed inputs on the ...
Tillmann Falck, Johan A. K. Suykens, Bart De Moor
CISS
2008
IEEE
14 years 3 months ago
On optimal training and beamforming in uncorrelated MIMO systems with feedback
—This paper studies the design and analysis of optimal training-based beamforming in uncorrelated multipleinput multiple-output (MIMO) channels with known Gaussian statistics. Fi...
Francisco Rubio, Dongning Guo, Michael L. Honig, X...
ICONIP
2007
13 years 10 months ago
Using Generalization Error Bounds to Train the Set Covering Machine
In this paper we eliminate the need for parameter estimation associated with the set covering machine (SCM) by directly minimizing generalization error bounds. Firstly, we consider...
Zakria Hussain, John Shawe-Taylor
ICML
2008
IEEE
14 years 9 months ago
Training SVM with indefinite kernels
Similarity matrices generated from many applications may not be positive semidefinite, and hence can't fit into the kernel machine framework. In this paper, we study the prob...
Jianhui Chen, Jieping Ye