#### Filter Results:

- Full text PDF available (19)

#### Publication Year

1984

2013

- This year (0)
- Last 5 years (1)
- Last 10 years (11)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

Learn More

- Ingo Steinwart, Don R. Hush, Clint Scovel
- Journal of Machine Learning Research
- 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 asâ€¦ (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 useâ€¦ (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 theâ€¦ (More)

- Bill G. Horne, Don R. Hush
- NIPS
- 1993

In this paper the efficiency of recurrent neural network implementations of m-state finite state machines will be explored. Specifically, it will be shown that the node complexity for theâ€¦ (More)

- Don R. Hush, Patrick Kelly, Clint Scovel, Ingo Steinwart
- Journal of Machine Learning Research
- 2006

We describe polynomialâ€“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. Theseâ€¦ (More)

- Bill G. Horne, Don R. Hush
- Neural Networks
- 1994

- Ingo Steinwart, Don R. Hush, Clint Scovel
- Journal of Machine Learning Research
- 2011

We develop, analyze, and test a training algorithm for suppo rt vector machine classifiers without offset. Key features of this algorithm are a new, statistica lly motivated stopping criterion, newâ€¦ (More)

- Don R. Hush
- Neural Computation
- 1999

This article proves that the task of computing near-optimal weights for sigmoidal nodes under the L1 regression norm is NP-Hard. For the special case where the sigmoid is piecewise linear, we prove aâ€¦ (More)

- Don R. Hush, Nasir Ahmed, Ruth David, Samuel D. Stearns
- IEEE Trans. Acoustics, Speech, and Signalâ€¦
- 1986

- Don R. Hush, Bill G. Horne
- IEEE Trans. Neural Networks
- 1998

This paper presents a computationally efficient algorithm for function approximation with piecewise linear sigmoidal nodes. A one hidden layer network is constructed one node at a time using theâ€¦ (More)