Regularization Networks and Support Vector Machines

作者: Theodoros Evgeniou , Massimiliano Pontil , Tomaso Poggio

DOI: 10.1023/A:1018946025316

关键词: Machine learningSemi-supervised learningReproducing kernel Hilbert spaceRegularization perspectives on support vector machinesSupport vector machineStructural risk minimizationLeast squares support vector machineArtificial intelligenceMathematicsSpecial caseRegularization (mathematics)

摘要: … In order to set the stage for the next two sections on regularization and Support Vector Machines, we outline here how we can justify the proper use of the RN and the SVM functionals (…

参考文章(94)
D. J. C. Mackay, Introduction to Gaussian processes NATO advanced study institute on generalization in neural networks and machine learning. pp. 133- 165 ,(1998)
M. Bertero, Regularization methods for linear inverse problems Lecture Notes in Mathematics. pp. 52- 112 ,(1986) , 10.1007/BFB0072660
Theodoros Evgeniou, Tomaso Poggio, Massimiliano Pontil, Luis Perez-Breva, Bounds on the Generalization Performance of Kernel Machine Ensembles international conference on machine learning. pp. 271- 278 ,(2000)
Vladimir Naumovich Vapnik, Estimation of Dependences Based on Empirical Data ,(2010)
Joseph W. Jerome, Review: Larry L. Schumaker, Spline functions: Basic theory Bulletin of the American Mathematical Society. ,vol. 6, pp. 238- 247 ,(1982) , 10.1090/S0273-0979-1982-14996-5
Tomaso Poggio, Federico Girosi, A Theory of Networks for Approximation and Learning Massachusetts Institute of Technology. ,(1989)
Filip M. Mulier, Vladimir Cherkassky, Learning from Data: Concepts, Theory, and Methods John Wiley & Sons, Inc.. ,(1998)
László Györfi, Luc Devroye, Gábor Lugosi, A Probabilistic Theory of Pattern Recognition ,(1996)