terça-feira, 8 de janeiro de 2013

2009d - Support Vectors Learning for Vector Field Reconstruction Marcos

Abstract—Sampled vector fields generally appear as measurements
of real phenomena. They can be obtained by the
use of a Particle Image Velocimetry acquisition device, or
as the result of a physical simulation, such as a fluid flow
simulation, among many examples. This paper proposes to
formulate the unstructured vector field reconstruction and
approximation through Machine-Learning. The machine learns
from the samples a global vector field estimation function
that could be evaluated at arbitrary points from the whole
domain. Using an adaptation of the Support Vector Regression
method for multi-scale analysis, the proposed method provides
a global, analytical expression for the reconstructed vector field
through an efficient non-linear optimization. Experiments on
artificial and real data show a statistically robust behavior of
the proposed technique.
Keywords-Discrete Vector Field; Support Vector Machine;

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