Don R. Hush

发表

Samuel D. Stearns, Don R. Hush, S. Stearns, 2016 .

Statistics for characterizing data on the periphery

Don R. Hush, James Theiler, D. Hush, 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

A Constructive Neural Network Algorithm for Function Approximation Using Locally Fit Sigmoids

Don R. Hush, Tim Draelos, D. Hush, 1998, Int. J. Artif. Intell. Tools.

Neural networks in fault detection: a case study

Domingo Docampo, Chaouki T. Abdallah, Don R. Hush, 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

An Oracle Inequality for Clipped Regularized Risk Minimizers

Don R. Hush, Ingo Steinwart, Clint Scovel, 2006, NIPS.

Weighted Order Statistic Classifiers with Large Rank-Order Margin

Don R. Hush, James Theiler, Reid B. Porter, 2003, ICML.

Wide-Area Motion Imagery

Don R. Hush, Andrew M. Fraser, Reid B. Porter, 2010, IEEE Signal Processing Magazine.

The recursive neural network and its applications in control theory

Don R. Hush, Chaouki T. Abdallah, Bill G. Horne, 1993 .

Recursive neural networks for signal processing and control

Chaouki T. Abdallah, Don R. Hush, Bill G. Horne, 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.

Ordered Hypothesis Machines

Don R. Hush, Reid B. Porter, G. Beate Zimmer, 2012, Journal of Mathematical Imaging and Vision.

Query by image example: The CANDID approach

Don R. Hush, Patrick M. Kelly, T. Michael Cannon, 1995 .

Algorithms for optimal dyadic decision trees

Don R. Hush, Reid B. Porter, D. Hush, 2010, Machine Learning.

Machine Learning with Data Dependent Hypothesis Classes

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

Error minimizing algorithms for nearest neighbor classifiers

Don R. Hush, Reid B. Porter, G. Beate Zimmer, 2011, Electronic Imaging.

On the node complexity of neural networks

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

Polynomial-Time Decomposition Algorithms for Support Vector Machines

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

Multiple instance learning using simple classifiers

Don R. Hush, Adam Cannon, 2004, 2004 International Conference on Machine Learning and Applications, 2004. Proceedings..

Interactive Machine Learning in Data Exploitation

Don R. Hush, James Theiler, Reid B. Porter, 2013, Computing in Science & Engineering.

Models of Analog Systems

Samuel D. Stearns, Don R. Hush, S. Stearns, 2016 .

KAPITEL 12. Entwurf analoger und digitaler Filter

Samuel D. Stearns, Don R. Hush, S. Stearns, 1999 .

Interactive DSP course development/teaching environment

Ramiro Jordan, Chaouki T. Abdallah, Don R. Hush, 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines

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

Density Level Detection is Classification

Don R. Hush, Ingo Steinwart, Clint Scovel, 2004, NIPS.

Detection of multiple sinusoids using an adaptive cascaded structure

Don R. Hush, Nasir Ahmed, G. R. Elliott, 1984, ICASSP.

An adaptive IIR structure for sinusoidal enhancement, frequency estimation, and detection

Samuel D. Stearns, Don R. Hush, Nasir Ahmed, 1986, IEEE Trans. Acoust. Speech Signal Process..

On the VC Dimension of Bounded Margin Classifiers

Don R. Hush, Clint Scovel, 2004, Machine Learning.

Optimal Rates for Regularized Least Squares Regression

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

A non-parametric diagnostic for exploring the relation between pairs of sampled data

Chaouki T. Abdallah, Don R. Hush, 1993 .

Bounds on the complexity of recurrent neural network implementations of finite state machines

Don R. Hush, Bill G. Horne, D. Hush, 1993, Neural Networks.

An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels

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

Error surfaces for multilayer perceptrons

Don R. Hush, Bill G. Horne, John M. Salas, 1992, IEEE Trans. Syst. Man Cybern..

Learning from dependent observations

Don R. Hush, Ingo Steinwart, Clint Scovel, 2007, J. Multivar. Anal..

Change detection for target detection and classification in video sequences

Don R. Hush, Gregory W. Donohoe, Nasir Ahmed, 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

Digitale Verarbeitung analoger Signale / Digital Signal Analysis

Samuel D. Stearns, Don R. Hush, S. Stearns, 1999 .

Function Classes That Approximate the Bayes Risk

Don R. Hush, Ingo Steinwart, Clint Scovel, 2006, COLT.

Gaps in Support Vector Optimization

Don R. Hush, Ingo Steinwart, Clint Scovel, 2007, COLT.

A Classification Framework for Anomaly Detection

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

Training a Sigmoidal Node Is Hard

Don R. Hush, D. Hush, 1999, Neural Computation.

The recursive neural network

Don R. Hush, Chaouki T. Abdallah, Bill G. Horne, 2012 .

Estimating a posteriori probability with P-type nodes

Don R. Hush, Bill G. Horne, 1990, 1990 IJCNN International Joint Conference on Neural Networks.

Network constraints and multi-objective optimization for one-class classification

Don R. Hush, M. M. Moya, Mary M. Moya, 1996, Neural Networks.

A new technique for adaptive frequency estimation and tracking

Don R. Hush, Delores M. Etter, 1987, IEEE Trans. Acoust. Speech Signal Process..

Selecting a restoration technique to minimize OCR error

Don R. Hush, Clint Scovel, Mike Fugate, 2003, IEEE Trans. Neural Networks.

Training SVMs Without Offset

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

Function Approximation with the Sweeping Hinge Algorithm

Don R. Hush, Fernando Lozano, Bill G. Horne, 1997, NIPS.

Efficient algorithms for function approximation with piecewise linear sigmoidal networks

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

Fat-Shattering of Affine Functions

Don R. Hush, Clint Scovel, 2004, Comb. Probab. Comput..

Stability of Unstable Learning Algorithms

Don R. Hush, Ingo Steinwart, Clint Scovel, 2007, Machine Learning.

Radial kernels and their reproducing kernel Hilbert spaces

Don R. Hush, James Theiler, Ingo Steinwart, 2010, J. Complex..

Pattern Recognition with Support Vector Machines

Samuel D. Stearns, Don R. Hush, S. Stearns, 2016 .

Stack Filter Classifiers

Don R. Hush, Reid B. Porter, G. Beate Zimmer, 2009, ISMM.

An overview of neural networks: Part II: Dynamic networks

Don R. Hush, Bill G. Horne, 1992 .

Error bounds in constructive approximation

Chaouki T. Abdallah, Don R. Hush, C. Abdallah, 1996 .