Related papers: Learning a regression function via Tikhonov regula…

There is a technical issue in the analysis that is not easily fixable. We, therefore, withdraw the submission. Sorry for the inconvenience.

The paper was withdrawn by the author. It contained various errors.

This paper has been withdrawn by the author due to a crucial error in equation (51).

This paper has been withdrawn by the author, due to a crucial error in the proof of Thm.1

This paper has been withdrawn by the author, due to a crucial error in the proof of Lemma 3.1.

This paper is withdrawn due to some errors, which are corrected in arXiv:0912.0071v4 [cs.LG].

The paper is withdrawn.

This paper has been withdrawn by the author, due to a crucial error in page 5.

The theory of spectral filtering is a remarkable tool to understand the statistical properties of learning with kernels. For least squares, it allows to derive various regularization schemes that yield faster convergence rates of the excess…

This paper has been withdrawn by the author due to essential mistakes in some previous versions.

This paper has been withdrawn by the author due to an error in section 7. There is a new version: arXiv:1011.3352.

This paper has been withdrawn by the author due to the presented idea is wrong.

This paper has been withdrawn by the author, due to a significant error in section 4.3.1.

This paper has been withdrawn due to its publication

We study recursive regularized learning algorithms in the reproducing kernel Hilbert space (RKHS) with non-stationary online data streams. We introduce the concept of random Tikhonov regularization path and decompose the tracking error of…

This paper has been withdrawn by the author due to a crucial problem in Lemma 3. This equation must be changed.

With the rapid growth of data, how to extract effective information from data is one of the most fundamental problems. In this paper, based on Tikhonov regularization, we propose an effective method for reconstructing the function and its…

This paper has been withdrawn.

In this paper, the classification algorithm arising from Tikhonov regularization is discussed. The main intention is to derive learning rates for the excess misclassification error according to the convex $\eta$-norm loss function…

This paper is withdrawn.