Support-vector networks

Margin-based classification offered a powerful approach to nonlinear learning.

Corinna Cortes and Vladimir Vapnik

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The contribution

Cortes and Vapnik developed support-vector networks for classification, combining a margin-based objective with a feature-space view of nonlinear decision boundaries. The paper became a central reference for statistical learning methods.

What this does not establish

A large margin is not a universal guarantee of accuracy on new data. Results depend on data, representation, regularization, and evaluation.

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The linked journal article was published in September 1995.

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The original work

Support-vector networks

Authors: Corinna Cortes and Vladimir Vapnik
Publication: Machine Learning, 20, 273–297
Source type: Journal article

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Learning representations by back-propagating errors (1986)
Cites Rumelhart, Hinton and Williams (1986), Nature 323, 533-536, using a variant title. The separately listed PDP chapter is a different work. Check the reference list.

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Publisher references

Aizerman, M., Braverman, E., & Rozonoer, L. (1964). Theoretical foundations of the potential function method in pattern recognition learning.Automation and Remote Control, 25:821–837. Anderson, T.W., & Bahadur, R.R. (1966). Classification into two multivariate normal distributions with different covariance matrices.Ann. Math. Stat., 33:420–431. Boser, B.E., Guyon, I., & Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. InProceedings of the Fifth Annual Workshop of Computational Learning Theory, 5, 144–152. Pittsburgh, ACM. Bottou, L., Cortes, C., Denker, J.S., Drucker, H., Guyon, I., Jackel, L.D., LeCun, Y., Sackinger, E., Simard, P., Vapnik, V., & Miller, U.A. (1994). Comparison of classifier methods: A case study in handwritten digit recognition.Proceedings of 12th International Conference on Pattern Recognition and Neural Network. Bromley, J., & Sackinger, E. (1991). Neural-network andk-nearest-neighbor classifiers. Technical Report 11359-910819-16TM, AT&T. Cournant, R., & Hilbert, D. (1953).Methods of Mathematical Physics, Interscience, New York. Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems.Ann. Eugenics, 7:111–132. LeCun, Y. (1985). Une procedure d'apprentissage pour reseau a seuil assymetrique.Cognitiva 85: A la Frontiere de l'Intelligence Artificielle des Sciences de la Connaissance des Neurosciences, 599–604, Paris. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., & Jackel, L.D. (1990). Handwritten digit recognition with a back-propagation network.Advances in Neural Information Processing Systems, 2, 396–404, Morgan Kaufman. Parker, D.B. (1985). Learning logic. Technical Report TR-47, Center for Computational Research in Economics and Management Science, Massachusetts Institute of Technology, Cambridge, MA. Rosenblatt, F. (1962).Principles of Neurodynamics, Spartan Books, New York. Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning internal representations by backpropagating errors.Nature, 323:533–536. Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1987). Learning internal representations by error propagation. In James L. McClelland & David E. Rumelhart (Eds.),Parallel Distributed Processing, 1, 318–362, MIT Press. Vapnik, V.N. (1982).Estimation of Dependences Based on Empirical Data, Addendum 1, New York: Springer-Verlag.

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