Years and Authors of Summarized Original Work
1992; Boser, Guyon, Vapnik
Problem Definition
In 1992 Vapnik and coworkers [1] proposed a supervised algorithm for classification that has since evolved into what are now known as support vector machines (SVMs) [2]: a class of algorithms for classification, regression, and other applications that represent the current state of the art in the field. Among the key innovations of this method were the explicit use of convex optimization, statistical learning theory, and kernel functions.
(a) The feature map simplifies the classification task. (b) A maximal margin hyperplane with its support vectors highlighted
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Recommended Reading
Boser B, Guyon I, Vapnik V (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on computational learning theory, Pittsburgh
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge. Book website: www.support-vector.net
Vapnik V (1995) The nature of statistical learning theory. Springer, New York
Cortes C, Vapnik V (1995) Support-vector network. Mach Learn 20:273–297
Hastie T, Rosset S, Tibshirani R, Zhu J (2004) The entire regularization path for the support vector machine. J Mach Learn Res 5:1391–1415
Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. Adv Neural Inf Process Syst (NIPS) 9:155–161. MIT
Platt J (1999) Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods support vector learning. MIT, Cambridge, pp 185–208
Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge. Book website: www.kernel-methods.net
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Joachims T (1998) Text categorization with support vector machines. In: Proceedings of European conference on machine learning (ECML), Chemnitz
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LeCun Y, Jackel LD, Bottou L, Brunot A, Cortes C, Denker JS, Drucker H, Guyon I, Muller UA, Sackinger E, Simard P, Vapnik V (1995) Comparison of learning algorithms for handwritten digit recognition. In: Fogelman-Soulie F, Gallinari P (eds) Proceedings international conference on artificial neural networks (ICANN), Paris, vol 2. EC2, pp 5360
Jaakkola TS, Haussler D (1999) Probabilistic kernel regression models. In: Proceedings of the 1999 Conference on AI and Statistics, Fort Lauderdale
Brown M, Grundy W, Lin D, Cristianini N, Sugnet C, Furey T, Ares M Jr, Haussler D (2000) Knowledge-based analysis of mircoarray gene expression data using support vector machines. Proc Natl Acad Sci 97(1):262–267
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Cristianini, N., Ricci, E. (2016). Support Vector Machines. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2864-4_415
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