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- 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, 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)

- Houman Owhadi, Clint Scovel, Timothy John Sullivan, Mike McKerns, Michael Ortiz
- SIAM Review
- 2013

We propose a rigorous framework for uncertainty quantification (UQ) in which the UQ objectives and its assumptions/information set are brought to the forefront. This framework, which we call optimal… (More)

- Ingo Steinwart, Clint Scovel
- COLT
- 2005

We establish learning rates to the Bayes risk for support vector machines (SVMs) using a regularization sequence λ n = n -α , where a ∈ (0,1) is arbitrary. Under a noise condition recently proposed… (More)

- Ingo Steinwart, Don R. Hush, Clint Scovel
- IEEE Transactions on Information Theory
- 2006

Although Gaussian radial basis function (RBF) kernels are one of the most often used kernels in modern machine learning methods such as support vector machines (SVMs), little is known about the… (More)

- Ingo Steinwart, Don R. Hush, Clint Scovel
- J. Mach. Learn. Res.
- 2011

We develop, analyze, and test a training algorithm for support vector machine classifiers without offset. Key features of this algorithm are a new, statistically motivated stopping criterion, new… (More)

- Don R. Hush, Clint Scovel
- Machine Learning
- 2003

This paper studies the convergence properties of a general class of decomposition algorithms for support vector machines (SVMs). We provide a model algorithm for decomposition, and prove necessary… (More)

- Don R. Hush, Patrick Kelly, Clint Scovel, Ingo Steinwart
- J. Mach. Learn. Res.
- 2006

We describe polynomial–time algorithms that produce appro ximate solutions with guaranteed accuracy for a class of QP problems that are used in the design of support vector machine classifiers. These… (More)

- James Theiler, Clint Scovel, Brendt Wohlberg, Bernard R. Foy
- IEEE Geoscience and Remote Sensing Letters
- 2010

We derive a class of algorithms for detecting anomalous changes in hyperspectral image pairs by modeling the data with elliptically contoured (EC) distributions. These algorithms are generalizations… (More)

- Adam Cannon, J. Mark Ettinger, Don R. Hush, Clint Scovel
- J. Mach. Learn. Res.
- 2002

We extend the VC theory of statistical learning to data dependent spaces of classifiers. This theory can be viewed as a decomposition of classifier design into two components; the first component is… (More)