相关论文: Learning a regression function via Tikhonov regula…

We exploit the similarities between Tikhonov regularization and Bayesian hierarchical models to propose a regularization scheme that acts like a distributed Tikhonov regularization where the amount of regularization varies from component to…

This paper has been withdrawn by the author due to the presented idea is wrong.

Sample reweighting is one of the most widely used methods for correcting the error of least squares learning algorithms in reproducing kernel Hilbert spaces (RKHS), that is caused by future data distributions that are different from the…

This paper has been withdrawn by the author, due to an error in Proposition 2.2.

This paper has been withdrawn by the author(s). The material contained in the paper will be published in a subtantially reorganized form, part of it is now included in math.QA/0510174

The paper was withdrawn because of its significant overlap with a paper appeared recently.

Bayesian regularization is a central tool in modern-day statistical and machine learning methods. Many applications involve high-dimensional sparse signal recovery problems. The goal of our paper is to provide a review of the literature on…

Regularization plays a pivotal role in ill-posed machine learning and inverse problems. However, the fundamental comparative analysis of various regularization norms remains open. We establish a small noise analysis framework to assess the…

The paper has been withdrawn because the research work is still in progress.

This paper has been withdrawn by the author for further investigation.

This paper has been temporarily withdrawn by the author(s),

These lecture notes for a graduate class present the regularization theory for linear and nonlinear ill-posed operator equations in Hilbert spaces. Covered are the general framework of regularization methods and their analysis via spectral…

This paper has been withdrawn by the author due to an error.

This paper has been withdrawn by the author due to the result being known

This paper is withdrawn. We found a mistake in Lemma 4.1

Regularized kernel methods such as support vector machines (SVM) and support vector regression (SVR) constitute a broad and flexible class of methods which are theoretically well investigated and commonly used in nonparametric…

This paper has been withdrawn by the author

This paper has been withdrawn by the author ali pourmohammad.

this paper has been withdrawn. A crucial estimate on a Lipschitz variant of the Kakeya maximal function has an incomplete proof.

This paper has been temporarily withdrawn for corrections.