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Wu Q, Zhou DX (2005) SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming Neural Comput 17:1160-1187

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Anthony M, Bartlett PL (1999) Neural network learning: Theoretical foundations
Aronszajn N (1950) Theory of reproducing kernels Transactions Of The American Mathematical Society 68:337-404
Barron AR (1990) Complexity regularization with applications to artificial neural networks Nonparametric functional estimation, Roussa G, ed. pp.561
Bartlett PL (1998) The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network IEEE Trans Inform Theory 44:525-536
Bartlett PL, Jordan MI, Mcauliffe JD (2003) Convexity, classification, and risk bounds Unpublished manuscript
Blanchard G, Bousquet O, Massart P (2004) Statistical performance of support vector machines Unpublished manuscript
Boser BE, Guyon I, Vapnik V (1992) A training algorithm for optimal margin classifiers Proceedings Of The Fifth Annual Workshop Of Computational Learning Theory 5:144-152
Bousquet O, Elisseeff A (2002) Stability and generalization J Mach Learn Res 2:499-526
Bradley PS, Mangasarian OL (2000) Massive data discrimination via linear support vector machines Optimization Methods And Software 13:1-10
Chen DR, Wu Q, Ying Y, Zhou DX (2004) Support vector machine soft margin classifiers: Error analysis J Mach Learn Res 5:1143-1175
Cortes C, Vapnik V (1995) Support-vector networks Mach Learn 20:273-297
Cristianini N, Shawe-taylor J (2000) An introduction to support vector machines
Cucker F, Smale S (2001) On the mathematical foundations of learning Bull Amer Math Soc 39:1-49
Devroye L, Gyorfi L, Lugosi G (1996) A probabilistic theory of pattern recognition
Evgeniou T, Pontil M, Poggio T (2000) Regularization networks and support vector machines Adv Comp Math 13:1-50
Kecman V, Hadzic I (2000) Support vector selection by linear programming Proc IJCNN 5:193-198
Lugosi G, Vayatis N (2004) On the Bayes-risk consistency of regularized boosting methods Ann Stat 32:30-55
Mendelson S (2002) Improving the sample complexity using global data IEEE Trans Inform Theory 48:1977-1991
Mukherjee S, Rifkin R, Poggio T (2002) Regression and classification with regularization Nonlinear estimation and classification, Denison DD:Hnasen MH:Holmes CC:Mallick B:Yu B, ed. pp.107
Niyogi P (1998) The informational complexity of learning
Niyogi P, Girosi F (1996) On the relationship between generalization error, hypothesis complexity, and sample complexity for radial basis functions Neural Comput 8:819-842
Pedroso JP, Murata N (2001) Support vector machines with different norms: Motivation, formulations and results Pattern Recognition Letters 22:1263-1272
Pontil M (2003) A note on different covering numbers in learning theory J Complexity 19:665-671
Rosasco L, De Vito E, Caponnetto A, Piana M, Verri A (2004) Are loss functions all the same? Neural Comput 16:1063-76 [Journal] [PubMed]
Smale S, Zhou DX (2003) Estimating the approximation error in learning theory Anal Appl 1:17-41
Smale S, Zhou DX (2004) Shannon sampling and function reconstruction from point values Bull Amer Math Soc 41:279-305
Steinwart I (2002) Support vector machines are universally consistent J Complexity 18:768-791
Steinwart I, Scovel C (2005) Fast Rates for Support Vector Machines Learning Theory, Auer, Peter and Meir, Ron, ed. pp.279
Tsybakov AB (2004) Optimal aggregation of classifiers in statistical learning Ann Stat 32:135-166
van_der_Vaart AW, Wellner JA (1996) Weak convergence and empirical processes
Vapnik V (1998) Statistical Learning Theory
Wahba G (1990) Splines models for observational data
Wu Q, Ying Y, Zhou D (2007) Multi-kernel regularized classifiers Journal of Complexity 23:108 - 134 [Journal]
Wu Q, Zhou DX (2004) Analysis of support vector machine classification Manuscript submitted for publication
Zhang T (2002) Covering number bounds of certain regularized linear function classes J Mach Learn Res 2:527-550
Zhang T (2004) Statistical behavior and consistency of classification methods based on convex risk minimization Ann Stat 32:56-85
Zhou DX (2002) The covering number in learning theory J Complexity 18:739-767
Zhou DX (2003) Capacity of reproducing kernel spaces in learning theory IEEE Trans Inform Theory 49:1743-1752
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