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ICML
2007
IEEE
16 years 3 months ago
Transductive support vector machines for structured variables
We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over a...
Alexander Zien, Ulf Brefeld, Tobias Scheffer
DAC
1997
ACM
15 years 7 months ago
Quadratic Placement Revisited
The “quadratic placement” methodology is rooted in [6] [14] [16] and is reputedly used in many commercial and in-house tools for placement of standard-cell and gate-array desi...
Charles J. Alpert, Tony F. Chan, Dennis J.-H. Huan...
ICCV
2005
IEEE
15 years 8 months ago
How Hard is 3-View Triangulation Really?
We present a solution for optimal triangulation in three views. The solution is guaranteed to find the optimal solution because it computes all the stationary points of the (maxi...
Henrik Stewénius, Frederik Schaffalitzky, D...
PVM
2005
Springer
15 years 8 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
HEURISTICS
2006
95views more  HEURISTICS 2006»
15 years 3 months ago
Hard and soft constraints for reasoning about qualitative conditional preferences
Many real life optimization problems are defined in terms of both hard and soft constraints, and qualitative conditional preferences. However, there is as yet no single framework f...
Carmel Domshlak, Steven David Prestwich, Francesca...