Интересно видит кое-кто историю распознавания образов
In 1936, R. A. Fisher suggested the first algorithm for pattern recognition (Fisher 1936).
Aronszajn (1950) introduced the ‘Theory of Reproducing Kernels’.
In 1957 Frank Rosenblatt invented a linear classifier called the perceptron (the simplest kind of feedforward neural network), see Rosenblatt (1962).
Vapnik and Lerner (1963) introduce the Generalized Portrait algorithm (the algorithm implemented by support vector machines is a nonlinear generalization of the Generalized Portrait algorithm).
Aizerman, Braverman and Rozonoer (1964) introduced the geometrical interpretation of the kernels as inner products in a feature space.
Vapnik and Chervonenkis (1964) further develop the Generalized Portrait algorithm.
Cover (1965) discussed large margin hyperplanes in the input space and also sparseness.
Similar optimisation techniques were used in pattern recognition by Mangasarian (1965).
The use of slack variables to overcome the problem of noise and nonseparability was introduced by Smith (1968).
Duda and Hart (1973) discuss large margin hyperplanes in the input space.
The field of ‘statistical learning theory’ began with Vapnik and Chervonenkis (1974) (in Russian).
SVMs can be said to have started when statistical learning theory was developed further with Vapnik (1979) (in Russian).
Wapnik and Tscherwonenkis (1979) wrote a German translation of Vapnik and Chervonenkis?s 1974 book.
Vapnik (1982) wrote an English translation of his 1979 book.
See also the PhD thesis by Hassoun (1986) for related early work.
Several statistical mechanics papers (for example Anlauf and Biehl (1989)) suggested using large margin hyperplanes in the input space.
Poggio and Girosi (1990) and Wahba (1990) discuss the use of kernels.
Bennett and Mangasarian (1992) improved upon Smith’s 1968 work on slack variables.
SVMs close to their current form were first introduced with a paper at the COLT 1992 conference (Boser, Guyon and Vapnik 1992).
In 1995 the soft margin classifier was introduced by Cortes and Vapnik (1995); in the same year the algorithm was extended to the case of regression by Vapnik (1995) in The Nature of Statistical Learning Theory.
The papers by Bartlett (1998) and Shawe-Taylor, et al. (1998) gave the first rigorous statistical bound on the generalisation of hard margin SVMs.
Shawe-Taylor and Cristianini (2000) gave statistical bounds on the generalisation of soft margin algorithms and for the regression case.
Links
Support Vector Learning: Bernhard Sch?pf
Bibliography
AIZERMAN, M. A., E. M. BRAVERMAN, and L. I. ROZONOER, 1964. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control, 25, 821–837.
ANLAUF, J. K., and M. BIEHL, 1989. The adatron: An adaptive perceptron algorithm. Europhysics Letters, 10(7), 687–692.
ARONSZAJN, N., 1950. Theory of reproducing kernels. Transactions of the American Mathematical Society, 68(3), 337–404.
BARTLETT, Peter L., 1998. The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network. IEEE Transactions on Information Theory, 44(2), 525–536.
BENNETT, Kristin P., and O. L. MANGASARIAN, 1992. Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software, 1, 23–34.
BOSER, Bernhard E., Isabelle M. GUYON, and Vladimir N. VAPNIK, 1992. A training algorithm for optimal margin classifiers. In: COLT ’92: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. New York, NY, USA: ACM Press, pp. 144–152.
CORTES, Corinna, and Vladimir VAPNIK, 1995. Support-vector networks. Machine Learning, 20(3), 273–297.
COVER, Thomas M., 1965. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, 14(3), 326–334.
DUDA, Richard O., and Peter E. HART, 1973. Pattern Classification and Scene Analysis. New York: John Wiley & Sons Inc.
FISHER, R. A., 1936. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 111–132.
HASSOUN, M. H., 1986. Optical Threshold Gates and Logical Signal Processing. Ph. D. thesis, Wayne State University, Detroit, USA.
MANGASARIAN, O. L., 1965. Linear and nonlinear separation of patterns by linear programming. Operations Research, 13(3), 444–452.
POGGIO, Tomaso, and Federico GIROSI, 1990. Networks for approximation and learning. Proceedings of the IEEE, 78(9), 1481–1497.
ROSENBLATT, Frank, 1962. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Washington DC: Spartan Books.
SHAWE-TAYLOR, John, et al., 1998. Structural risk minimization over datadependent hierarchies. IEEE Transactions on Information Theory, 44(5), 1926–1940.
SHAWE-TAYLOR, John, and Nello CRISTIANINI, 2000. Margin distribution and soft margin. In: Alexander J. SMOLA, et al., eds. Advances in Large Margin Classifiers. The MIT Press, pp. 349–358.
SMITH, F. W., 1968. Pattern classifier design by linear programming. IEEE Transactions on Computers, C-17(4), 367–372.
VAPNIK, V., 1979. Estimation of Dependences Based on Empirical Data [in Russian]. Moscow: Nauka.
VAPNIK, Vladimir, 1982. Estimation of Dependences Based on Empirical Data. Springer Verlag.
VAPNIK, V., and A. CHERVONENKIS, 1964. A note on one class of perceptrons. Automation and Remote Control, 25.
VAPNIK, V., and A. LERNER, 1963. Pattern recognition using generalized portrait method. Automation and Remote Control, 24, 774–780.
VAPNIK, Vladimir N., 1995. The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc.
VAPNIK, V. N., and A. Ya. CHERVONENKIS, 1974. Teoriya raspoznavaniya obrazov: Statisticheskie problemy obucheniya. (Russian) [Theory of pattern recognition: Statistical problems of learning]. Moscow: Nauka.
WAHBA, Grace, 1990. Spline Models for Observational Data. Volume 59 of CBMS-NSF Regional Conference Series in Applied Mathematics. Philadelphia, PA, USA: SIAM: Society for Industrial and Applied Mathematics.
WAPNIK, W. N., and A. J. TSCHERWONENKIS, 1979. Theorie der Zeichenerkennung. (German) [Theory of pattern recognition]. Berlin: Akademie-Verlag.
http://www.svms.org/history.html
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