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In this work we propose a general procedure for analyzing global minima of arbitrary mathematical programs which is based in probability measures and moments theory. We give a general characterization of global minima of arbitrary programs, and as a particular case, we characterize the global minima of unconstrained one dimensional polynomial programs by using a particular semidefinite program.
Support vector machines have recently attracted much attention in the machine learning and optimization communities for their remarkable generalization ability. An open problem, however, is the selection of the optimal kernel matrix for regression problems. Recently, a means to compute the optimal kernel matrix for pattern classification using semidefinite programming has been introduced [7]. In this...
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