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Qualitative Robustness of Support Vector Machines
Support vector machines have attracted much attention in theoretical and in
applied statistics. Main topics of recent interest are consistency, learning
rates and robustness. In this article, it is shown that support vector machines
are qualitatively robust. Since support vector machines can be represented by a
functional on the set of all probability measures, qualitative robustness is
proven by showing that this functional is continuous with respect to the
topology generated by weak convergence of probability measures. Combined with
the existence and uniqueness of support vector machines, our results show that
support vector machines are the solutions of a well-posed mathematical problem
in Hadamard's sense
A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description
We present a new non-parametric method to quantify morphologies of galaxies
based on a particular family of learning machines called support vector
machines. The method, that can be seen as a generalization of the classical CAS
classification but with an unlimited number of dimensions and non-linear
boundaries between decision regions, is fully automated and thus particularly
well adapted to large cosmological surveys. The source code is available for
download at http://www.lesia.obspm.fr/~huertas/galsvm.html To test the method,
we use a seeing limited near-infrared ( band, ) sample observed
with WIRCam at CFHT at a median redshift of . The machine is trained
with a simulated sample built from a local visually classified sample from the
SDSS chosen in the high-redshift sample's rest-frame (i band, ) and
artificially redshifted to match the observing conditions. We use a
12-dimensional volume, including 5 morphological parameters and other
caracteristics of galaxies such as luminosity and redshift. We show that a
qualitative separation in two main morphological types (late type and early
type) can be obtained with an error lower than 20% up to the completeness limit
of the sample () which is more than 2 times better that what would
be obtained with a classical C/A classification on the same sample and indeed
comparable to space data. The method is optimized to solve a specific problem,
offering an objective and automated estimate of errors that enables a
straightforward comparison with other surveys.Comment: 11 pages, 7 figures, 3 tables. Submitted to A&A. High resolution
images are available on reques
Qualitative Robustness in Bayesian Inference
The practical implementation of Bayesian inference requires numerical
approximation when closed-form expressions are not available. What types of
accuracy (convergence) of the numerical approximations guarantee robustness and
what types do not? In particular, is the recursive application of Bayes' rule
robust when subsequent data or posteriors are approximated? When the prior is
the push forward of a distribution by the map induced by the solution of a PDE,
in which norm should that solution be approximated? Motivated by such
questions, we investigate the sensitivity of the distribution of posterior
distributions (i.e. posterior distribution-valued random variables, randomized
through the data) with respect to perturbations of the prior and data
generating distributions in the limit when the number of data points grows
towards infinity
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