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PVM
2005
Springer
13 years 10 months ago
Some Improvements to a Parallel Decomposition Technique for Training Support Vector Machines
We consider a parallel decomposition technique for solving the large quadratic programs arising in training the learning methodology Support Vector Machine. At each iteration of th...
Thomas Serafini, Luca Zanni, Gaetano Zanghirati
ECML
2004
Springer
13 years 8 months ago
Efficient Hyperkernel Learning Using Second-Order Cone Programming
The kernel function plays a central role in kernel methods. Most existing methods can only adapt the kernel parameters or the kernel matrix based on empirical data. Recently, Ong e...
Ivor W. Tsang, James T. Kwok
ICML
2004
IEEE
14 years 5 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. ...
JMLR
2010
182views more  JMLR 2010»
12 years 11 months ago
Quadratic Programming Feature Selection
Identifying a subset of features that preserves classification accuracy is a problem of growing importance, because of the increasing size and dimensionality of real-world data se...
Irene Rodriguez-Lujan, Ramón Huerta, Charle...
MP
2008
99views more  MP 2008»
13 years 5 months ago
Efficient robust optimization for robust control with constraints
This paper proposes an efficient computational technique for the optimal control of linear discrete-time systems subject to bounded disturbances with mixed polytopic constraints o...
Paul J. Goulart, Eric C. Kerrigan, Daniel Ralph