相关论文: Learning a regression function via Tikhonov regula…
This paper has been withdrawn by the author due to a crucial error in the formulation.
This paper has been withdrawn by the authors due to an unlikely results.
Tikhonov regularization with square-norm penalty for linear forward operators has been studied extensively in the literature. However, the results on convergence theory are based on technical proofs and difficult to interpret. It is also…
We consider a statistical inverse learning problem, where the task is to estimate a function $f$ based on noisy point evaluations of $Af$, where $A$ is a linear operator. The function $Af$ is evaluated at i.i.d. random design points $u_n$,…
Despite a variety of available techniques the issue of the proper regularization parameter choice for inverse problems still remains one of the biggest challenges. The main difficulty lies in constructing a rule, allowing to compute the…
In this paper, we consider the nonlinear ill-posed inverse problem with noisy data in the statistical learning setting. The Tikhonov regularization scheme in Hilbert scales is considered to reconstruct the estimator from the random noisy…
This paper has been withdrawn due to an error found by Dana Angluin and Lev Reyzin.
This paper has been withdrawn by the author because overcame by arXiv:0910.4694
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This paper has been withdrawn because of serious errors.
The paper has been withdrawn
This paper has been withdrawn by the author due to a new work in [arXiv:0901.0456v4] which can contain the results in this paper.
State-of-the-art image reconstruction often relies on complex, highly parameterized deep architectures. We propose an alternative: a data-driven reconstruction method inspired by the classic Tikhonov regularization. Our approach iteratively…
This paper has been withdrawn by the author due to a mistake in the proof of the main theorem.
This paper has been withdrawn by the author due to a gap in the proof of the main result.
This paper has been withdrawn by the author due to a crucial error.
A main drawback of classical Tikhonov regularization is that often the parameters required to apply theoretical results, e.g., the smoothness of the sought-after solution and the noise level, are unknown in practice. In this paper we…
This paper has been withdrawn.
This paper has been withdrawn