Detection of multiple sinusoids using an adaptive cascaded structure. - Nasir Ahmed 001; Don R. Hush; G. R. Elliott; R. Joseph Fogler (ICASSP, 1984)

Detection and identification of sinusoids in broadband noise via a parallel recursive ALE. - Don R. Hush; Nasir Ahmed 001 (ICASSP, 1985)

An adaptive IIR structure for sinusoidal enhancement, frequency estimation, and detection. - Don R. Hush; Nasir Ahmed 001; Ruth David; Samuel D. Stearns (IEEE Trans. Acoust. Speech Signal Process., 1986)

A new technique for adaptive frequency estimation and tracking. - Delores M. Etter; Don R. Hush (IEEE Trans. Acoust. Speech Signal Process., 1987)

Change detection for target detection and classification in video sequences. - Gregory W. Donohoe; Don R. Hush; Nasir Ahmed 001 (ICASSP, 1988)

Improving the learning rate of back-propagation with the gradient reuse algorithm. - Don R. Hush; John M. Salas (ICNN, 1988)

Statistical analysis of a change detector based on image modeling of difference picture. - Daechul Park; Don R. Hush (ICASSP, 1990)

Estimating a posteriori probability with P-type nodes. - Bill G. Horne; Don R. Hush (IJCNN, 1990)

Error surfaces for multilayer perceptrons. - Don R. Hush; Bill G. Horne; John M. Salas (IEEE Trans. Syst. Man Cybern., 1992)

Progress in supervised neural networks. - Don R. Hush; Bill G. Horne (IEEE Signal Process. Mag., 1993)

On the node complexity of neural networks. - Bill G. Horne; Don R. Hush (Neural Networks, 1994)

Query by Image Example: The Comparison Algorithm for Navigating Digital Image Databases (CANDID) Approach. - Patrick M. Kelly; T. Michael Cannon; Don R. Hush (Storage and Retrieval for Image and Video Databases (SPIE), 1995)

A constructive neural network algorithm for function approximation. - Tim Draelos; Don R. Hush (ICNN, 1996)

Bounds on the complexity of recurrent neural network implementations of finite state machines. - Bill G. Horne; Don R. Hush (Neural Networks, 1996)

Network constraints and multi-objective optimization for one-class classification. - Mary M. Moya; Don R. Hush (Neural Networks, 1996)

Interactive DSP course development/teaching environment. - Chaouki T. Abdallah; Dalton Soares Arantes; Gregory L. Heileman; Don R. Hush; Ramiro Jordan; Roberto de Alencar Lotufo; Neeraj Magotra; L. Howard Pollard; Edl Schamiloglu; Robert Whitman (ICASSP, 1997)

Function Approximation with the Sweeping Hinge Algorithm. - Don R. Hush; Fernando Lozano; Bill G. Horne (NIPS, 1997)

A Constructive Neural Network Algorithm for Function Approximation Using Locally Fit Sigmoids. - Tim Draelos; Don R. Hush (Int. J. Artif. Intell. Tools, 1998)

Efficient algorithms for function approximation with piecewise linear sigmoidal networks. - Don R. Hush; Bill G. Horne (IEEE Trans. Neural Networks, 1998)

Training a Sigmoidal Node Is Hard. - Don R. Hush (Neural Comput., 1999)

Product property monitoring for a batch polymerization reaction system. - Kenneth R. Muske; James W. Howse; Don R. Hush (ACC, 2001)

On the VC Dimension of Bounded Margin Classifiers. - Don R. Hush; Clint Scovel (Mach. Learn., 2001)

Machine Learning with Data Dependent Hypothesis Classes. - Adam Cannon; J. Mark Ettinger; Don R. Hush; Clint Scovel (J. Mach. Learn. Res., 2002)

Weighted Order Statistic Classifiers with Large Rank-Order Margin. - Reid B. Porter; Damian Eads; Don R. Hush; James Theiler (ICML, 2003)

Polynomial-Time Decomposition Algorithms for Support Vector Machines. - Don R. Hush; Clint Scovel (Mach. Learn., 2003)

Selecting a restoration technique to minimize OCR error. - Mike Cannon; Mike Fugate; Don R. Hush; Clint Scovel (IEEE Trans. Neural Networks, 2003)

Multiple instance learning using simple classifiers. - Adam Cannon; Don R. Hush (ICMLA, 2004)

Density Level Detection is Classification. - Ingo Steinwart; Don R. Hush; Clint Scovel (NIPS, 2004)

Fat-Shattering of Affine Functions. - Don R. Hush; Clint Scovel (Comb. Probab. Comput., 2004)

A Classification Framework for Anomaly Detection. - Ingo Steinwart; Don R. Hush; Clint Scovel (J. Mach. Learn. Res., 2005)

Function Classes That Approximate the Bayes Risk. - Ingo Steinwart; Don R. Hush; Clint Scovel (COLT, 2006)

An Oracle Inequality for Clipped Regularized Risk Minimizers. - Ingo Steinwart; Don R. Hush; Clint Scovel (NIPS, 2006)

QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines. - Don R. Hush; Patrick Kelly; Clint Scovel; Ingo Steinwart (J. Mach. Learn. Res., 2006)

An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels. - Ingo Steinwart; Don R. Hush; Clint Scovel (IEEE Trans. Inf. Theory, 2006)

Gaps in Support Vector Optimization. - Nikolas List; Don R. Hush; Clint Scovel; Ingo Steinwart (COLT, 2007)

Stability of Unstable Learning Algorithms. - Don R. Hush; Clint Scovel; Ingo Steinwart (Mach. Learn., 2007)

Optimal Rates for Regularized Least Squares Regression. - Ingo Steinwart; Don R. Hush; Clint Scovel (COLT, 2009)

Stack Filter Classifiers. - Reid B. Porter; G. Beate Zimmer; Don R. Hush (ISMM, 2009)

Learning from dependent observations. - Ingo Steinwart; Don R. Hush; Clint Scovel (J. Multivar. Anal., 2009)

Statistics for characterizing data on the periphery. - James Theiler; Don R. Hush (IGARSS, 2010)

Radial kernels and their reproducing kernel Hilbert spaces. - Clint Scovel; Don R. Hush; Ingo Steinwart; James Theiler (J. Complex., 2010)

Algorithms for optimal dyadic decision trees. - Don R. Hush; Reid B. Porter (Mach. Learn., 2010)

Wide-Area Motion Imagery. - Reid B. Porter; Andrew M. Fraser; Don R. Hush (IEEE Signal Process. Mag., 2010)

A comparison of methods for estimating broadband noise in the frequency domain. - Don R. Hush; Norma H. Pawley; Kary L. Myers; Bob Nemzek (ACSCC, 2011)

Interactive image quantification tools in nuclear material forensics. - Reid B. Porter; Christy Ruggiero; Don R. Hush; Neal R. Harvey; Patrick Kelly; Wayne Scoggins; Lav Tandon (Image Processing: Machine Vision Applications, 2011)

Error minimizing algorithms for nearest neighbor classifiers. - Reid B. Porter; Don R. Hush; G. Beate Zimmer (Image Processing: Algorithms and Systems, 2011)

Learning from Only Positive and Unlabeled Data to Detect Lesions in Vascular CT Images. - Maria A. Zuluaga; Don R. Hush; Edgar J. F. Delgado Leyton; Marcela Hernández Hoyos; Maciej Orkisz (MICCAI (3), 2011)

Training SVMs Without Offset. - Ingo Steinwart; Don R. Hush; Clint Scovel (J. Mach. Learn. Res., 2011)

Ordered Hypothesis Machines. - G. Beate Zimmer; Don R. Hush; Reid B. Porter (J. Math. Imaging Vis., 2012)

Interactive Machine Learning in Data Exploitation. - Reid B. Porter; James Theiler; Don R. Hush (Comput. Sci. Eng., 2013)

Predicting Student Enrollment based on Student and College Characteristics. - Ahmad Slim; Don R. Hush; Tushar Ojha; Terry Babbitt (EDM, 2018)

An Automated Framework to Recommend A Suitable Academic Program, Course and Instructor. - Ahmad Slim; Don R. Hush; Tushar Ojha; Chaouki T. Abdallah; Gregory L. Heileman; Georges El-Howayek (BigDataService, 2019)

Maximizing University Enrollment Using Institutional-Based Aid Scholarship. - Ahmad Slim; Don R. Hush; Tushar Ojha; Georges El-Howayek; Chaouki T. Abdallah; Terry Babbitt (ICMLA, 2021)

Characterizing Extra Credit Accumulation by Undergraduate Students. - Tushar Ojha; Don R. Hush (IEEE Access, 2025)