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- Ingo Steinwart
- J. Mach. Learn. Res.
- 2001

In this article we study the generalization abilities of several classifiers of support vector machine (SVM) type using a certain class of kernels that we call universal. It is shown that the soft… (More)

- Ingo Steinwart, Don R. Hush, Clint Scovel
- COLT
- 2009

We establish a new oracle inequality for kernelbased, regularized least squares regression methods, which uses the eigenvalues of the associated integral operator as a complexity measure. We then use… (More)

- Ingo Steinwart
- IEEE Transactions on Information Theory
- 2005

It is shown that various classifiers that are based on minimization of a regularized risk are universally consistent, i.e., they can asymptotically learn in every classification task. The role of the… (More)

- Ingo Steinwart, C. Scovel
- 2007

For binary classification we establish learning rates up to the order of n −1 for support vector machines (SVMs) with hinge loss and Gaussian RBF kernels. These rates are in terms of two assumptions… (More)

- Ingo Steinwart, Don R. Hush, Clint Scovel
- J. Mach. Learn. Res.
- 2005

One way to describe anomalies is by saying that anomalies are not concentrated. This leads to the problem of finding level sets for the data generating density. We interpret this learning problem as… (More)

- Ingo Steinwart
- J. Mach. Learn. Res.
- 2003

Support vector machines (SVMs) construct decision functions that are linear combinations of kernel evaluations on the training set. The samples with non-vanishing coefficients are called support… (More)

- Ingo Steinwart
- J. Complexity
- 2002

We show that support vector machines of the 1-norm soft margin type are universally consistent provided that the regularization parameter is chosen in a distinct manner and the kernel belongs to a… (More)

- Ingo Steinwart
- 2007

AbstractMany learning problems are described by a risk functional which in turn is defined by a loss function, and
a straightforward and widely known approach to learn such problems is to minimize a… (More)

- Ingo Steinwart, Clint Scovel
- COLT
- 2005

We establish learning rates to the Bayes risk for support vector machines (SVMs) using a regularization sequence ${\it \lambda}_{n}={\it n}^{-\rm \alpha}$, where ${\it \alpha}\in$(0,1) is arbitrary.… (More)