N. List, D. Hush, C. Scovel and I. Steinwart, Gaps in support vector optimization. In Proceedings of the 20th Conference on Learning Theory, 336-348, 2007. Los Alamos National Laboratory Technical Report LA-UR-07-0621.   [   Abstract   ]  

I. Steinwart and A. Christmann, How SVMs can estimate quantiles and the median. In Neural Information Processing Systems 20, to appear. Los Alamos National Laboratory Technical Report LA-UR-07-6041, 2007.   [   Abstract   |   PDF (211 KB)   ]  

I. Steinwart and M. Anghel, An SVM approach for forecasting the evolution of an unknown ergodic dynamical system from observations with unknown noise. Annals of Statistics, to appear. Los Alamos National Laboratory Technical Report LA-UR-07-1829, 2007.   [   Abstract   |   PDF (325 KB)   ]  

A. Christmann and I. Steinwart, Consistency of Kernel Based Quantile Regression. Applied Stochastic Models in Business and Industry, to appear. Los Alamos National Laboratory Technical Report LA-UR-06-7110, 2006.   [   Abstract   |   PDF (227 KB)   ]  

A. Christmann, I. Steinwart, and M. Hubert, Robust Learning from Bites for Data Mining. Computational Statistics & Data Analysis, Vol. 52, pp. 347-361, 2007. Los Alamos National Laboratory Technical Report LA-UR-06-2769.   [   Abstract   |   PDF (308 KB)   ]  

I. Steinwart, D. Hush, and C. Scovel, Learning from Dependent Observations. Los Alamos National Laboratory Technical Report LA-UR-06-3507, submitted for publication, 2006.   [   Abstract   |   PDF (348 KB)   ]  

I. Steinwart, D. Hush, and C. Scovel, An Oracle Inequality for Clipped Regularized Risk Minimizers. In Neural Information Processing Systems 19, pp. 1321-1328, (2007). Los Alamos National Laboratory Technical Report LA-UR-06-3981.   [   Abstract   |   PDF (121 KB)   ]  

I. Steinwart, D. Hush, and C. Scovel, A new Concentration Result for Regularized Risk Minimizers. High-dimensional Probability IV, in IMS Lecture Notes--Monograph Series, Vol. 51, pp. 260-275, 2006. Los Alamos National Laboratory Technical Report LA-UR-05-9403, 2005.   [   Abstract   |   PDF (253 KB)   ]  

I. Steinwart, How to compare different loss functions and their risks. Constructive Approximation, Vol. 26, pp. 225-287, 2007. Los Alamos National Laboratory Technical Report LA-UR-05-7016.   [   Abstract   |   PDF (515 KB)   ]  

D. Hush and J. Howse, Anomaly Detection on Graphs. Los Alamos National Laboratory Technical Report LA-UR-05-8440, 2005.   [   Abstract   |   Postscript (382 KB)   |   PDF (145 KB)   ]  

D. Hush, C. Scovel, and I. Steinwart, Polynomial Time Algorithms for Computing Approximate SVM Solutions with Guaranteed Accuracy. Los Alamos National Laboratory Technical Report LA-UR-05-7738, 2005.   [   Abstract   |   Postscript (288 KB)   |   PDF (227 KB)   ]  

C. Scovel, D. Hush, and I. Steinwart, Approximate Duality. Journal of Optimization Theory and Applications, to appear. Los Alamos National Laboratory Technical Report LA-UR-05-6755.   [   Abstract   |   Postscript (153 KB)   |   PDF (174 KB)   ]  

D. Hush, P. Kelly, C. Scovel and I. Steinwart, Provably Fast Algorithms for Anomaly Detection. Los Alamos National Laboratory Technical Report LA-UR-05-4367, 2005.   [   Abstract   |   Postscript (248 KB)   |   PDF (247 KB)   ]  

D. Hush, P. Kelly, C. Scovel and I. Steinwart, QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines. Journal of Machine Learning Research 7(May):733-769, 2006. Los Alamos National Laboratory Technical Report LA-UR-05-5165.   [   Abstract   |   Postscript (787 KB)   |   PDF (627 KB)   ]  

A. Christmann and I. Steinwart, Consistency and Robustness of Kernel Based Regression. Bernoulli, Vol. 13, pp. 799-819, 2007. Los Alamos National Laboratory Technical Report LA-UR-04-8797.   [   Abstract   |   PDF (395 KB)   ]  

I. Steinwart and C. Scovel, Fast Rates for Support Vector Machines using Gaussian Kernels. Annals of Statistics, Vol. 35, pp. 575-607, 2007. Los Alamos National Laboratory Technical Report LA-UR-04-8796.   [   Abstract   |   PDF (403 KB)   ]  

I. Steinwart, D. Hush, and C. Scovel, An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels Kernels. IEEE Transactions on Information Theory, Vol. 52, pp. 4635-4643, 2006. Los Alamos National Laboratory Technical Report LA-UR-04-8274.   [   Abstract   |   PDF (244 KB)   ]  

I. Steinwart, D. Hush, and C. Scovel, A classification framework for anomaly detection. Journal of Machine Learning Research, Vol. 6, pp. 211-232, 2005. Los Alamos National Laboratory Technical Report LA-UR-04-4716.   [   Abstract   |   PostScript (535 MB)   |   PDF (243 KB)   ]  

D. Hush, C. Scovel, and I. Steinwart, Stability of Unstable Learning Algorithms. Machine Learning Journal, Vol. 67, pp. 197-206, 2007. Los Alamos National Laboratory Technical Report LA-UR-03-4845, 2003.   [   Abstract   |   PostScript (413 KB)   |   PDF (322 KB)   ]  

C. Scovel, D. Hush, C. Scovel and I. Steinwart, Learning Rates for Density Level Detection. Analysis and Applications, Vol. 3, No. 4 (2005) 356-371. Los Alamos National Laboratory Technical Report LA-UR-05-2088.   [   Abstract   |   Postscript (262 KB)   |   PDF (287 KB)   ]  

C. Scovel and I. Steinwart, Fast Rates for Support Vector Machines. In Proceedings of the 18th Conference on Learning Theory (COLT 2005), Bertinoro, Italy, 2005. Los Alamos National Laboratory Technical Report LA-UR-05-0451.   [   Abstract   ]  

I. Steinwart and C. Scovel, Fast Rates to Bayes for Kernel Methods. In Neural Information Processing Systems 17, pp. 1345-1352, (2005). Los Alamos National Laboratory Technical Report LA-UR-04-3767.   [   Abstract   |   PDF (92 KB)   ]  

I. Steinwart, D. Hush, and C. Scovel, Density Level Detection is Classification. In Neural Information Processing Systems 17, pp. 1337-1344, (2005). Los Alamos National Laboratory Technical Report LA-UR-04-3768.   [   Abstract   |   PDF (104 KB)   ]  

I. Steinwart, Consistency of Support Vector Machines and other Regularized Kernel Machines. IEEE Transactions on Information Theory, Vol. 51, pp. 128-142, 2005.   [   Abstract   ]  

I. Steinwart, Sparseness of Support Vector Machines -- Some Asymptotically Sharp Bounds. In Neural Information Processing Systems 16, pp. 1069-1076, (2004). Los Alamos National Laboratory Technical Report LA-UR-03-3643.   [   Abstract   |   PostScript (121 KB)   |   PDF (98 KB)   ]  

A. Christmann and I. Steinwart, On robustness properties of convex risk minimization methods for pattern recognition. Journal of Machine Learning Research, Vol. 5, pp. 1007-1034, 2004. Los Alamos National Laboratory Technical Report LA-UR-03-3892.   [   Abstract   |   PostScript (1.04 MB)   |   PDF (433 KB)   ]  

D. Hush and C. Scovel, Learning with the Ratchet Algorithm. Los Alamos National Laboratory Technical Report LA-UR-03-2033.   [   Abstract   |   PostScript (213 KB)   |   PDF (214 KB)   ]  

D. Hush and C. Scovel, Concentration of the Hypergeometric Distribution. Statistics and Probability Letters, Volume 75, Issue 2 , 15 November 2005, Pages 127-132. Los Alamos National Laboratory Technical Report LA-UR-03-1353.   [   Abstract   |   PostScript (126 KB)   |   PDF (138 KB)   ]  

D. Hush and C. Scovel, Fat-shattering of affine functions. Combinatorics Probability and Computing, Vol. 13, No. 3, pp. 353-360, 2004. Los Alamos National Laboratory Technical Report LA-UR-03-0937.   [   Abstract   |   PostScript (160 KB)   |   PDF (160 KB)   ]  

A. Cannon, J. Howse, D. Hush, and C. Scovel, Simple Classifiers. Los Alamos National Laboratory Technical Report LA-UR-03-0193, 2003.   [   Abstract   |   PostScript (639 KB)   |   PDF (535 KB)   ]  

M. Cannon, M. Fugate, D. Hush, and C. Scovel. Selecting a Restoration Technique to Minimize OCR Error. IEEE Transactions on Neural Networks, v. 14, No 3. pp. 478--490, May 2003. Los Alamos National Laboratory Technical Report LA-UR-01-6860.   [   Abstract   |   PostScript (376 KB)   |   PDF (414 KB)   ]  

A. Cannon, J. Howse, D. Hush, and C. Scovel, Learning with the Neyman-Pearson and min-max criteria. Los Alamos National Laboratory Technical Report LA-UR-02-2951, 2002.   [   Abstract   |   PostScript (208 KB)   |   PDF (196 KB)   ]  

J. Howse, D. Hush, and C. Scovel, Linking learning strategies and performance for support vector machines. Los Alamos National Laboratory Technical Report LA-UR-02-1933, 2002.   [   Abstract   |   PostScript (474 KB)   |   PDF (477 KB)   ]  

A. Cannon, M. Ettinger, D. Hush, and C. Scovel, Machine learning with data dependent hypothesis classes. Journal of Machine Learning Research, Vol. 2, pp. 335-358, 2002. Los Alamos Technical Report LAUR-01-2583.   [   Abstract   |   PostScript (322 KB)   |   PDF (363 KB)   ]  

D. Hush and C. Scovel, Polynomial-time decomposition algorithms for support vector machines. Machine Learning, Vol. 51, pp. 51-71, 2003. Los Alamos National Laboratory Technical Report LA-UR-00-3800.   [   Abstract   |   PostScript (287 KB)   |   PDF (323 KB)   ]  

D. Hush, and C. Scovel, Support Vector Machines. Los Alamos Technical Report LAUR-00-579, 2000.   [   Abstract   |   PostScript (187 KB)   |   PDF (323 KB)   ]  

I. Steinwart, Sparseness of Support Vector Machines. Journal of Machine Learning Research, Vol. 4, pp. 1071-1105, 2003.   [   Abstract   |   PDF (306 KB)   ]  

M. Fugate, D. Hush, C. Scovel, and R. Christensen, An equivalence relation between parallel calibration and principle component regression. Journal of Chemometrics, Vol. 16, pp. 68-70, 2002. Los Alamos National Laboratory Technical Report LA-UR-99-6600.   [   Abstract   |   PostScript (80 KB)   |   PDF (97 KB)   ]  

D. Hush and C. Scovel, On the VC dimension of bounded margin classifiers. Machine Learning, v. 45, pp. 33-44, 2001. Los Alamos National Laboratory Technical Report LA-UR-00-3800.   [   Abstract   |   PostScript (160 KB)   |   PDF (199 KB)   ]  

D. Hush, Training a sigmoidal node is hard. Neural Computation, v. 11, pp. 1249-1260, 1999. (UNM)   [   Abstract   |   PostScript (190 KB)   |   PDF (219 KB)   ]  

D.R. Hush, and B. Horne, Efficient algorithms for function approximation with piecewise linear sigmoidal networks. IEEE Trans. Neural Networks, Vol. 9, No. 6, pp. 1129-1141, 1998. (UNM)   [   Abstract   |   PostScript (424 KB)   |   PDF (392 KB)   ]