Don R. Hush

	2012
32		G. Beate Zimmer, Don R. Hush, Reid B. Porter: Ordered Hypothesis Machines. Journal of Mathematical Imaging and Vision 43(2): 121-134 (2012)
	2011
31		Ingo Steinwart, Don R. Hush, Clint Scovel: Training SVMs Without Offset. Journal of Machine Learning Research 12: 141-202 (2011)
	2010
30		James Theiler, Don R. Hush: Statistics for characterizing data on the periphery. IGARSS 2010: 4764-4767
29		Clint Scovel, Don R. Hush, Ingo Steinwart, James Theiler: Radial kernels and their reproducing kernel Hilbert spaces. J. Complexity 26(6): 641-660 (2010)
28		Don R. Hush, Reid B. Porter: Algorithms for optimal dyadic decision trees. Machine Learning 80(1): 85-107 (2010)
	2009
27		Ingo Steinwart, Don R. Hush, Clint Scovel: Optimal Rates for Regularized Least Squares Regression. COLT 2009
26		Reid B. Porter, G. Beate Zimmer, Don R. Hush: Stack Filter Classifiers. ISMM 2009: 282-294
25		Ingo Steinwart, Don R. Hush, Clint Scovel: Learning from dependent observations. J. Multivariate Analysis 100(1): 175-194 (2009)
	2007
24		Nikolas List, Don R. Hush, Clint Scovel, Ingo Steinwart: Gaps in Support Vector Optimization. COLT 2007: 336-348
23		Don R. Hush, Clint Scovel, Ingo Steinwart: Stability of Unstable Learning Algorithms. Machine Learning 67(3): 197-206 (2007)
	2006
22		Ingo Steinwart, Don R. Hush, Clint Scovel: Function Classes That Approximate the Bayes Risk. COLT 2006: 79-93
21		Ingo Steinwart, Don R. Hush, Clint Scovel: An Oracle Inequality for Clipped Regularized Risk Minimizers. NIPS 2006: 1321-1328
20		Ingo Steinwart, Don R. Hush, Clint Scovel: An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels. IEEE Transactions on Information Theory 52(10): 4635-4643 (2006)
19		Don R. Hush, Patrick Kelly, Clint Scovel, Ingo Steinwart: QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines. Journal of Machine Learning Research 7: 733-769 (2006)
	2005
18		Ingo Steinwart, Don R. Hush, Clint Scovel: A Classification Framework for Anomaly Detection. Journal of Machine Learning Research 6: 211-232 (2005)
	2004
17		Adam Cannon, Don R. Hush: Multiple instance learning using simple classifiers. ICMLA 2004: 123-128
16		Ingo Steinwart, Don R. Hush, Clint Scovel: Density Level Detection is Classification. NIPS 2004
15		Don R. Hush, Clint Scovel: Fat-Shattering of Affine Functions. Combinatorics, Probability & Computing 13(3): 353-360 (2004)
	2003
14		Reid B. Porter, Damian Eads, Don R. Hush, James Theiler: Weighted Order Statistic Classifiers with Large Rank-Order Margin. ICML 2003: 600-607
13		Mike Cannon, Mike Fugate, Don R. Hush, Clint Scovel: Selecting a restoration technique to minimize OCR error. IEEE Transactions on Neural Networks 14(3): 478-490 (2003)
12		Don R. Hush, Clint Scovel: Polynomial-Time Decomposition Algorithms for Support Vector Machines. Machine Learning 51(1): 51-71 (2003)
	2002
11		Adam Cannon, J. Mark Ettinger, Don R. Hush, Clint Scovel: Machine Learning with Data Dependent Hypothesis Classes. Journal of Machine Learning Research 2: 335-358 (2002)
	2001
10		Don R. Hush, Clint Scovel: On the VC Dimension of Bounded Margin Classifiers. Machine Learning 45(1): 33-44 (2001)
	1999
9		Don R. Hush: Training a Sigmoidal Node Is Hard. Neural Computation 11(5): 1249-1260 (1999)
	1998
8		Don R. Hush, Bill G. Horne: Efficient algorithms for function approximation with piecewise linear sigmoidal networks. IEEE Transactions on Neural Networks 9(6): 1129-1141 (1998)
7		Timothy Draelos, Don R. Hush: A Constructive Neural Network Algorithm for Function Approximation Using Locally Fit Sigmoids. International Journal on Artificial Intelligence Tools 7(2): 373-398 (1998)
	1997
6		Don R. Hush, Fernando Lozano, Bill G. Horne: Function Approximation with the Sweeping Hinge Algorithm. NIPS 1997
	1996
5		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)
4		Mary M. Moya, Don R. Hush: Network constraints and multi-objective optimization for one-class classification. Neural Networks 9(3): 463-474 (1996)
	1995
3		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
2		Bill G. Horne, Don R. Hush: On the node complexity of neural networks. Neural Networks 7(9): 1413-1426 (1994)
	1993
1		Bill G. Horne, Don R. Hush: Bounds on the Complexity of Recurrent Neural Network Implementations of Finite State Machines. NIPS 1993: 359-366

Coauthor Index

1	Adam Cannon	[11] [17]
2	Mike Cannon	[13]
3	T. Michael Cannon	[3]
4	Timothy Draelos	[7]
5	Damian Eads	[14]
6	J. Mark Ettinger	[11]
7	Mike Fugate	[13]
8	Bill G. Horne	[1] [2] [5] [6] [8]
9	Patrick Kelly	[19]
10	Patrick M. Kelly	[3]
11	Nikolas List	[24]
12	Fernando Lozano	[6]
13	Mary M. Moya	[4]
14	Reid B. Porter	[14] [26] [28] [32]
15	Clint Scovel	[10] [11] [12] [13] [15] [16] [18] [19] [20] [21] [22] [23] [24] [25] [27] [29] [31]
16	Ingo Steinwart	[16] [18] [19] [20] [21] [22] [23] [24] [25] [27] [29] [31]
17	James Theiler	[14] [29] [30]
18	G. Beate Zimmer	[26] [32]

Colors in the list of coauthors

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