An equivalence between sparse approximation and Support Vector Machines (1997)

by Federico Girosi
Venue:A.I. Memo 1606, MIT Arti cial Intelligence Laboratory
Citations:167 - 6 self

Active Bibliography

40 A unified framework for Regularization Networks and Support Vector Machines – Theodoros Evgeniou, Massimiliano Pontil - 1999
215 Regularization networks and support vector machines – Theodoros Evgeniou, Massimiliano Pontil, Tomaso Poggio - 2000
1 Learning with Kernel Machine Architectures – Theodoros Evgeniou, Tomaso Poggio, Helen Whitaker, Professor Brain, Cognitive Sciences, Arthur C. Smith - 2000
2 Notes on PCA, Regularization, Sparsity and Support Vector Machines – Tomaso Poggio, Federico Girosi - 1998
79 The mathematics of learning: Dealing with data – Tomaso Poggio, Steve Smale - 2003
32 A Sparse Representation for Function Approximation – Tomaso Poggio, Federico Girosi - 1998
18 A Review of Kernel Methods in Machine Learning – Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola - 2006
257 Regularization Theory and Neural Networks Architectures – Federico Girosi, Michael Jones, Tomaso Poggio - 1995
Bayesian Approach To Support Vector Machines – Chu Wei - 2003
119 The Connection between Regularization Operators and Support Vector Kernels – Alex J. Smola, Bernhard Schölkopf, Klaus-Robert Müller - 1998
1 A review of RKHS methods in machine learning – Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola - 2006
26 Regularized Principal Manifolds – Alexander J. Smola, Robert C. Williamson - 2001
308 A tutorial on support vector regression – Alex J. Smola, Bernhard Schölkopf - 2004
5 Mathematical Programming Approaches To Machine Learning And Data Mining – Paul S. Bradley - 1998
15 Support vector machine soft margin classifiers: Error analysis – Di-rong Chen, Qiang Wu, Yiming Ying, Ding-xuan Zhou - 2004
On the V gamma dimension for regression in Reproducing Kernel Hilbert Spaces – Theodoros Evgeniou, Massimiliano Pontil - 1999
1 Neural Networks in Economics: Background, Applications and New Developments – Ralf Herbrich, Max Keilbach, Thore Graepel, Peter Bollmann-Sdorra, Klaus Obermayer - 1998
36 Incorporating Prior Information in Machine Learning by Creating Virtual Examples – P. Niyogi, F. Girosi, T. Poggio - 1998
2 Faithful Representations and Moments of Satisfaction: Probabilistic Methods in Learning and Logic – Lidror Troyansky, Prof Naftali Tishby - 1998