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Don R. Hush
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2020 – today
- 2021
- [c28]Ahmad Slim, Don R. Hush, Tushar Ojha, Georges El-Howayek, Chaouki T. Abdallah, Terry Babbitt:
Maximizing University Enrollment Using Institutional-Based Aid Scholarship. ICMLA 2021: 1209-1214
2010 – 2019
- 2019
- [c27]Ahmad Slim, Don R. Hush, Tushar Ojha, Chaouki T. Abdallah
, Gregory L. Heileman, Georges El-Howayek:
An Automated Framework to Recommend A Suitable Academic Program, Course and Instructor. BigDataService 2019: 145-150 - 2018
- [c26]Ahmad Slim, Don R. Hush, Tushar Ojha, Terry Babbitt:
Predicting Student Enrollment based on Student and College Characteristics. EDM 2018 - 2013
- [j26]Reid B. Porter, James Theiler, Don R. Hush:
Interactive Machine Learning in Data Exploitation. Comput. Sci. Eng. 15(5): 12-20 (2013) - 2012
- [j25]G. Beate Zimmer, Don R. Hush, Reid B. Porter:
Ordered Hypothesis Machines. J. Math. Imaging Vis. 43(2): 121-134 (2012) - 2011
- [j24]Ingo Steinwart, Don R. Hush, Clint Scovel:
Training SVMs Without Offset. J. Mach. Learn. Res. 12: 141-202 (2011) - [c25]Don R. Hush, Norma H. Pawley, Kary L. Myers
A comparison of methods for estimating broadband noise in the frequency domain. ACSCC 2011: 316-320 - [c24]Reid B. Porter, Christy Ruggiero, Don R. Hush, Neal R. Harvey
Interactive image quantification tools in nuclear material forensics. Image Processing: Machine Vision Applications 2011: 787708 - [c23]Reid B. Porter, Don R. Hush, G. Beate Zimmer:
Error minimizing algorithms for nearest neighbor classifiers. Image Processing: Algorithms and Systems 2011: 787005 - [c22]Maria A. Zuluaga
Learning from Only Positive and Unlabeled Data to Detect Lesions in Vascular CT Images. MICCAI (3) 2011: 9-16 - 2010
- [j23]Clint Scovel, Don R. Hush, Ingo Steinwart
Radial kernels and their reproducing kernel Hilbert spaces. J. Complex. 26(6): 641-660 (2010) - [j22]Don R. Hush, Reid B. Porter:
Algorithms for optimal dyadic decision trees. Mach. Learn. 80(1): 85-107 (2010) - [j21]Reid B. Porter, Andrew M. Fraser
Wide-Area Motion Imagery. IEEE Signal Process. Mag. 27(5): 56-65 (2010) - [c21]James Theiler, Don R. Hush:
Statistics for characterizing data on the periphery. IGARSS 2010: 4764-4767
2000 – 2009
- 2009
- [j20]Ingo Steinwart
Learning from dependent observations. J. Multivar. Anal. 100(1): 175-194 (2009) - [c20]Ingo Steinwart, Don R. Hush, Clint Scovel:
Optimal Rates for Regularized Least Squares Regression. COLT 2009 - [c19]Reid B. Porter, G. Beate Zimmer, Don R. Hush:
Stack Filter Classifiers. ISMM 2009: 282-294 - 2007
- [j19]Don R. Hush, Clint Scovel, Ingo Steinwart
Stability of Unstable Learning Algorithms. Mach. Learn. 67(3): 197-206 (2007) - [c18]Nikolas List, Don R. Hush, Clint Scovel, Ingo Steinwart
Gaps in Support Vector Optimization. COLT 2007: 336-348 - 2006
- [j18]Don R. Hush, Patrick Kelly, Clint Scovel, Ingo Steinwart:
QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines. J. Mach. Learn. Res. 7: 733-769 (2006) - [j17]Ingo Steinwart
An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels. IEEE Trans. Inf. Theory 52(10): 4635-4643 (2006) - [c17]Ingo Steinwart
Function Classes That Approximate the Bayes Risk. COLT 2006: 79-93 - [c16]Ingo Steinwart, Don R. Hush, Clint Scovel:
An Oracle Inequality for Clipped Regularized Risk Minimizers. NIPS 2006: 1321-1328 - 2005
- [j16]Ingo Steinwart, Don R. Hush, Clint Scovel:
A Classification Framework for Anomaly Detection. J. Mach. Learn. Res. 6: 211-232 (2005) - 2004
- [j15]Don R. Hush, Clint Scovel:
Fat-Shattering of Affine Functions. Comb. Probab. Comput. 13(3): 353-360 (2004) - [c15]Adam Cannon, Don R. Hush:
Multiple instance learning using simple classifiers. ICMLA 2004: 123-128 - [c14]Ingo Steinwart, Don R. Hush, Clint Scovel:
Density Level Detection is Classification. NIPS 2004: 1337-1344 - 2003
- [j14]Don R. Hush, Clint Scovel:
Polynomial-Time Decomposition Algorithms for Support Vector Machines. Mach. Learn. 51(1): 51-71 (2003) - [j13]Mike Cannon, Mike Fugate, Don R. Hush, Clint Scovel:
Selecting a restoration technique to minimize OCR error. IEEE Trans. Neural Networks 14(3): 478-490 (2003) - [c13]Reid B. Porter, Damian Eads, Don R. Hush, James Theiler:
Weighted Order Statistic Classifiers with Large Rank-Order Margin. ICML 2003: 600-607 - 2002
- [j12]Adam Cannon, J. Mark Ettinger, Don R. Hush, Clint Scovel:
Machine Learning with Data Dependent Hypothesis Classes. J. Mach. Learn. Res. 2: 335-358 (2002) - 2001
- [j11]Don R. Hush, Clint Scovel:
On the VC Dimension of Bounded Margin Classifiers. Mach. Learn. 45(1): 33-44 (2001) - [c12]Kenneth R. Muske, James W. Howse, Don R. Hush:
Product property monitoring for a batch polymerization reaction system. ACC 2001: 987-992
1990 – 1999
- 1999
- [j10]Don R. Hush:
Training a Sigmoidal Node Is Hard. Neural Comput. 11(5): 1249-1260 (1999) - 1998
- [j9]Tim Draelos, Don R. Hush:
A Constructive Neural Network Algorithm for Function Approximation Using Locally Fit Sigmoids. Int. J. Artif. Intell. Tools 7(2): 373-398 (1998) - [j8]Don R. Hush, Bill G. Horne:
Efficient algorithms for function approximation with piecewise linear sigmoidal networks. IEEE Trans. Neural Networks 9(6): 1129-1141 (1998) - 1997
- [c11]Chaouki T. Abdallah
Interactive DSP course development/teaching environment. ICASSP 1997: 2249-2252 - [c10]Don R. Hush, Fernando Lozano, Bill G. Horne:
Function Approximation with the Sweeping Hinge Algorithm. NIPS 1997: 535-541 - 1996
- [j7]Bill G. Horne, Don R. Hush:
Bounds on the complexity of recurrent neural network implementations of finite state machines. Neural Networks 9(2): 243-252 (1996) - [j6]Mary M. Moya, Don R. Hush:
Network constraints and multi-objective optimization for one-class classification. Neural Networks 9(3): 463-474 (1996) - [c9]Tim Draelos, Don R. Hush:
A constructive neural network algorithm for function approximation. ICNN 1996: 50-55 - 1995
- [c8]Patrick M. Kelly, T. Michael Cannon, Don R. Hush:
Query by Image Example: The Comparison Algorithm for Navigating Digital Image Databases (CANDID) Approach. Storage and Retrieval for Image and Video Databases (SPIE) 1995: 238-248 - 1994
- [j5]Bill G. Horne, Don R. Hush:
On the node complexity of neural networks. Neural Networks 7(9): 1413-1426 (1994) - 1993
- [j4]Don R. Hush, Bill G. Horne:
Progress in supervised neural networks. IEEE Signal Process. Mag. 10(1): 8-39 (1993) - [c7]Bill G. Horne, Don R. Hush:
Bounds on the Complexity of Recurrent Neural Network Implementations of Finite State Machines. NIPS 1993: 359-366 - 1992
- [j3]Don R. Hush, Bill G. Horne, John M. Salas:
Error surfaces for multilayer perceptrons. IEEE Trans. Syst. Man Cybern. 22(5): 1152-1161 (1992) - 1990
- [c6]Daechul Park, Don R. Hush:
Statistical analysis of a change detector based on image modeling of difference picture. ICASSP 1990: 2185-2188 - [c5]Bill G. Horne, Don R. Hush:
Estimating a posteriori probability with P-type nodes. IJCNN 1990: 691-696
1980 – 1989
- 1988
- [c4]Gregory W. Donohoe, Don R. Hush, Nasir Ahmed:
Change detection for target detection and classification in video sequences. ICASSP 1988: 1084-1087 - [c3]Don R. Hush, John M. Salas:
Improving the learning rate of back-propagation with the gradient reuse algorithm. ICNN 1988: 441-447 - 1987
- [j2]Delores M. Etter, Don R. Hush:
A new technique for adaptive frequency estimation and tracking. IEEE Trans. Acoust. Speech Signal Process. 35(4): 561-564 (1987) - 1986
- [j1]Don R. Hush, Nasir Ahmed, Ruth David, Samuel D. Stearns:
An adaptive IIR structure for sinusoidal enhancement, frequency estimation, and detection. IEEE Trans. Acoust. Speech Signal Process. 34(6): 1380-1390 (1986) - 1985
- [c2]Don R. Hush, Nasir Ahmed:
Detection and identification of sinusoids in broadband noise via a parallel recursive ALE. ICASSP 1985: 1193-1196 - 1984
- [c1]Nasir Ahmed, Don R. Hush, G. R. Elliott, R. Joseph Fogler:
Detection of multiple sinusoids using an adaptive cascaded structure. ICASSP 1984: 199-202
Coauthor Index
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