1,834 research outputs found
Reproducing kernel Hilbert spaces and variable metric algorithms in PDE constrained shape optimisation
In this paper we investigate and compare different gradient algorithms
designed for the domain expression of the shape derivative. Our main focus is
to examine the usefulness of kernel reproducing Hilbert spaces for PDE
constrained shape optimisation problems. We show that radial kernels provide
convenient formulas for the shape gradient that can be efficiently used in
numerical simulations. The shape gradients associated with radial kernels
depend on a so called smoothing parameter that allows a smoothness adjustment
of the shape during the optimisation process. Besides, this smoothing parameter
can be used to modify the movement of the shape. The theoretical findings are
verified in a number of numerical experiments
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning
This paper presents a general vector-valued reproducing kernel Hilbert spaces
(RKHS) framework for the problem of learning an unknown functional dependency
between a structured input space and a structured output space. Our formulation
encompasses both Vector-valued Manifold Regularization and Co-regularized
Multi-view Learning, providing in particular a unifying framework linking these
two important learning approaches. In the case of the least square loss
function, we provide a closed form solution, which is obtained by solving a
system of linear equations. In the case of Support Vector Machine (SVM)
classification, our formulation generalizes in particular both the binary
Laplacian SVM to the multi-class, multi-view settings and the multi-class
Simplex Cone SVM to the semi-supervised, multi-view settings. The solution is
obtained by solving a single quadratic optimization problem, as in standard
SVM, via the Sequential Minimal Optimization (SMO) approach. Empirical results
obtained on the task of object recognition, using several challenging datasets,
demonstrate the competitiveness of our algorithms compared with other
state-of-the-art methods.Comment: 72 page
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