Regularization networks and support vector machines

Regularization networks and support vector machines (2000)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Theodoros Evgeniou , Massimiliano Pontil , Tomaso Poggio

Venue:	Advances in Computational Mathematics
Citations:	215 - 28 self

BibTeX

@INPROCEEDINGS{Evgeniou00regularizationnetworks,
    author = {Theodoros Evgeniou and Massimiliano Pontil and Tomaso Poggio},
    title = {Regularization networks and support vector machines},
    booktitle = {Advances in Computational Mathematics},
    year = {2000},
    pages = {1--50},
    publisher = {MIT Press}
}

Years of Citing Articles

Bookmark

OpenURL

Abstract

Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization and Support Vector Machines. We review both formulations in the context of Vapnik’s theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics. The emphasis is on regression: classification is treated as a special case.

Citations

6696	The Nature of Statistical Learning Theory - Vapnik - 1995
3341	Pattern Classification and Scene Analysis - Duda, Hart - 1973
2716	Neural networks: A comprehensive foundation - Haykin - 1994
1656	CJC: A tutorial on support vector machines for pattern recognition - Burges - 1998
1515	A Theory of the Learnable - Valiant - 1984
1491	Support-vector networks - Cortes, Vapnik - 1995
1418	lectures on wavelets - Daubechies, Ten - 1992
1089	Atomic decomposition by basis pursuit - Chen, Donoho, et al. - 1998
989	Spline models for observational data - Wahba - 1990
892	Modeling by shortest data description - Rissanen - 1978
816	On the uniform convergence of relative frequencies of events to their probabilities. Theory of Prob. and its - Vapnik, Chervonenkis - 1971
801	A probabilistic theory of pattern recognition - Devroye, Györfi, et al. - 1996
688	Estimation of dependencies based on empirical data - Vapnik - 1982
683	Emergence of simple-cell receptive field properties by learning a sparse code for natural images - Olshausen, Field - 1996
593	Solution of IllPosed problems - Tikhonov, Arsenin - 1977
570	Networks for approximation and learning - Poggio, Girosi - 1990
560	Theory of reproducing kernels - Aronszajn - 1950
478	Constructive Approximation - Devore, Lorentz - 1993
457	Convergence of Stochastic Processes - Pollard - 1984
416	Entropy-based algorithms for best-basis selection - 13Coifman, Wickerhauser - 1992
291	Spline Functions: Basic Theory - Schumaker - 1981
257	Regularization theory and neural networks architectures - Girosi, Jones, et al. - 1995
250	An information maximization approach to blind separation and blind deconvo-lution. Neural Comput - Bell, TW - 1995
243	Interpolation of Scattered Data: Distance Matrix and Conditionally Positive Definite Functions - Micchelli - 1986
221	Structural risk minimization over data-dependent hierarchies - Shawe-Taylor, Bartlett, et al. - 1940
215	Computational vision and regularization theory - Poggio, Torre, et al. - 1985
215	Natural gradient works efficiently in learning - Amari - 1998
214	Efficient pattern recognition using a new transformation distance - Simard, LeCun, et al. - 1993
193	Learning from Data: Concepts, Theory, and Methods - Cherkassky, Mulier - 1998
182	Efficient distributionfree learning of probabilistic concepts - Kearns, Schapire - 1994
175	Scale-sensitive dimensions, uniform convergence, and learnability - Alon, Ben-David, et al. - 1997
170	A Theory of Networks for Approximation and Learning - Poggio, Girosi - 1989
167	An equivalence between sparse approximation and support vector machines - Girosi - 1998
166	Statistical Field Theory - Parisi - 1988
163	Probabilistic solution of ill-posed problems in computational vision - Marroquin, Mitter, et al. - 1987
156	The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network - Bartlett
152	Ill-posed problems in early vision - Bertero, Poggio, et al. - 1988
150	The theory of radial basis function approximation - Powell - 1990
131	Methods of Mathematical Physics - Hilbert, Courant - 1986
129	Approximation of functions - Lorentz - 1966
116	Matching pursuit in a time-frequency dictionary - Mallat, Zhang - 1993
111	Contributions to the problem of approximation of equidistant data by analytic functions - Schoenberg - 1946
105	Generalization performance of support vector machines and other pattern classifiers - Bartlett, Shawe-Taylor, et al. - 1999
101	Local learning algorithms - Bottou, Vapnik - 1992
101	An Experimental and Theoretical Comparison of Model Selection Methods - Kearns, Mansour, et al. - 1999
92	On Basis Pursuit - 6Chen, Donoho - 1994
90	Probabilistic kernel regression models - Jaakkola, Haussler - 1999
84	The relationship between variable selection and data augmentation and a method of prediction - Allen - 1974
84	Introduction to gaussian processes - MacKay - 1998
80	Interpolation and Approximation by Radial and Related Functions, Approximation Theory - Dyn - 1989