Abstract. The aim of this article is to characterize the saturation spaces that appear in inverse problems. Such spaces are defined for a regularization method and a rate of conver-gence of the estimation part of the inverse problem depends on their definition. Here we prove that it is possible to define these spaces as regularity spaces, independent of the choice of the approximation method. Moreover, this intrinsec definition enables us to provide minimax rate of convergence under such assumptions. 1
The book collects and contributes new results on the theory and practice of ill-posed inverse proble...
Many works have shown that strong connections relate learning from examples to regularization techni...
Inverse problems arise whenever one tries to calculate a required quantity from given measurements o...
Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle ...
Esta Tesis abarca el estudio de métodos de regularización para problemas inversos mal condicionados ...
Many works related learning from examples to regularization techniques for inverse problems, emphasi...
We consider the solution of ill-posed inverse problems using regularization with tolerances. In part...
In this paper we consider discrete inverse problems for which noise becomes negligible compared to d...
This paper is concerned with the ubiquitous inverse problem of recovering an unknown function u from...
International audienceWe study a non-linear statistical inverse problem, where we observe the noisy ...
Many works related learning from examples to regularization techniques for inverse prob- lems, empha...
Projet IDENTThis paper is devoted to the introduction and analysis of regularization in state space ...
International audienceDue to the ill-posedness of inverse problems, it is important to make use of m...
Many works have shown that strong connections relate learning from examples to regularization techni...
Published in at http://dx.doi.org/10.1214/07-EJS115 the Electronic Journal of Statistics (http://www...
The book collects and contributes new results on the theory and practice of ill-posed inverse proble...
Many works have shown that strong connections relate learning from examples to regularization techni...
Inverse problems arise whenever one tries to calculate a required quantity from given measurements o...
Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle ...
Esta Tesis abarca el estudio de métodos de regularización para problemas inversos mal condicionados ...
Many works related learning from examples to regularization techniques for inverse problems, emphasi...
We consider the solution of ill-posed inverse problems using regularization with tolerances. In part...
In this paper we consider discrete inverse problems for which noise becomes negligible compared to d...
This paper is concerned with the ubiquitous inverse problem of recovering an unknown function u from...
International audienceWe study a non-linear statistical inverse problem, where we observe the noisy ...
Many works related learning from examples to regularization techniques for inverse prob- lems, empha...
Projet IDENTThis paper is devoted to the introduction and analysis of regularization in state space ...
International audienceDue to the ill-posedness of inverse problems, it is important to make use of m...
Many works have shown that strong connections relate learning from examples to regularization techni...
Published in at http://dx.doi.org/10.1214/07-EJS115 the Electronic Journal of Statistics (http://www...
The book collects and contributes new results on the theory and practice of ill-posed inverse proble...
Many works have shown that strong connections relate learning from examples to regularization techni...
Inverse problems arise whenever one tries to calculate a required quantity from given measurements o...