Abstract
Support vector machines represent a state-of-the-art paradigm, which has nevertheless been tackled by a number of other approaches in view of the development of a superior hybridized technique. It is also the proposal of present chapter to bring support vector machines together with evolutionary computation, with the aim to offer a simplified solving version for the central optimization problem of determining the equation of the hyperplane deriving from support vector learning. The evolutionary approach suggested in this chapter resolves the complexity of the optimizer, opens the ’blackbox’ of support vector training and breaks the limits of the canonical solving component.
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References
Bosch, R.A., Smith, J.A.: Separating hyperplanes and the authorship of the disputed federalist papers. American Mathematical Monthly 105, 601–608 (1998)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, New Jersey (1999)
Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 1, 273–297 (1995)
Mierswa, I.: Evolutionary learning with kernels: A generic solution for large margin problems. In: Proc. of the Genetic and Evolutionary Computation Conference, vol. 1, pp. 1553–1560 (2006)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Boser, B.E., Guyon, I.M., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, vol. 1, pp. 11–152 (1992)
Courant, R., Hilbert, D.: Methods of Mathematical Physics. Wiley Interscience, Hoboken (1970)
Mercer, J.: Functions of positive and negative type and their connection with the theory of integral equations. Transactions of the London Philosophical Society (A) 209, 415–446 (1908)
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13, 415–425 (2004)
Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large margin dags for multiclass classification. Proc. of Neural Information Processing Systems 1, 547–553 (2000)
Smola, A.J., Scholkopf, B.: A tutorial on support vector regression. Technical Report NC2-TR-1998-030. NeuroCOLT2 Technical Report Series (1998)
Rosipal, R.: Kernel-based Regression and Objective Nonlinear Measures to Access Brain Functioning. PhD thesis Applied Computational Intelligence Research Unit School of Information and Communications Technology University of Paisley, Scotland (2001)
Stoean, R.: Support vector machines. An evolutionary resembling approach. Universitaria Publishing House Craiova (2008)
Friedrichs, F., Igel, C.: Evolutionary tuning of multiple svm parameters. In: Proc. 12th European Symposium on Artificial Neural Networks, vol. 1, pp. 519–524 (2004)
Howley, T., Madden, M.G.: The genetic evolution of kernels for support vector machine classifiers. In: Proc. of 15th Irish Conference on Artificial Intelligence and Cognitive Science 1 (2004), http://www.it.nuigalway.ie/m_madden/profile/pubs.html
Jun, S.H., Oh, K.W.: An evolutionary statistical learning theory. International Journal of Computational Intelligence 3, 249–256 (2006)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Stoean, R., Preuss, M., Stoean, C., Dumitrescu, D.: Concerning the potential of evolutionary support vector machines. In: Proc. of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 1436–1443 (2007)
Stoean, R., Dumitrescu, D., Preuss, M., Stoean, C.: Different techniques of multi-class evolutionary support vector machines. Proc. of Bio-Inspired Computing: Theory and Applications 1, 299–306 (2006)
Stoean, R., Stoean, C., Preuss, M., Dumitrescu, D.: Evolutionary multi-class support vector machines for classification. In: Proceedings of International Conference on Computers and Communications - ICCC 2006, Baile Felix Spa - Oradea, Romania, vol. 1, pp. 423–428 (2006)
Stoean, R., Dumitrescu, D., Stoean, C.: Nonlinear evolutionary support vector machines. application to classification. Studia Babes-Bolyai, Seria Informatica LI, pp. 3–12 (2006)
Stoean, R., Dumitrescu, D.: Evolutionary linear separating hyperplanes within support vector machines. Scientific Bulletin, University of Pitesti, Mathematics and Computer Science Series 11, 75–84 (2005)
Stoean, R., Dumitrescu, D.: Linear evolutionary support vector machines for separable training data. Annals of the University of Craiova, Mathematics and Computer Science Series 33, 141–146 (2006)
Stoean, R., Preuss, M., Dumitrescu, D., Stoean, C.: ε - evolutionary support vector regression. In: Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2006, vol. 1, pp. 21–27 (2006)
Stoean, R., Preuss, M., Dumitrescu, D., Stoean, C.: Evolutionary support vector regression machines. In: IEEE Postproc. of the 8th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, vol. 1, pp. 330–335 (2006)
Stoean, R.: An evolutionary support vector machines approach to regression. In: Proc. of 6th International Conference on Artificial Intelligence and Digital Communications, vol. 1, pp. 54–61 (2006)
Stoean, R., Stoean, C., Preuss, M., El-Darzi, E., Dumitrescu, D.: Evolutionary support vector machines for diabetes mellitus diagnosis. In: Proceedings of IEEE Intelligent Systems 2006, London, UK, vol. 1, pp. 182–187 (2006)
Stoean, R., Stoean, C., Preuss, M., Dumitrescu, D.: Evolutionary support vector machines for spam filtering. In: Proc. of RoEduNet IEEE International Conference, vol. 1, pp. 261–266 (2006)
Stoean, R., Stoean, C., Preuss, M., Dumitrescu, D.: Evolutionary detection of separating hyperplanes in e-mail classification. Acta Cibiniensis LV, 41–46 (2007)
Stoean, R., Stoean, C., Preuss, M., Dumitrescu, D.: Forecasting soybean diseases from symptoms by means of evolutionary support vector machines. Phytologia Balcanica 12 (2006)
Mierswa, I.: Making indefinite kernel learning practical, technical report. Technical report. Artificial Intelligence Unit, Department of Computer Science, University of Dortmund (2006)
Bartz-Beielstein, T.: Experimental research in evolutionary computation - the new experimentalism. Natural Computing Series. Springer, Heidelberg (2006)
Perez-Cruz, F., Figueiras-Vidal, A.R., Artes-Rodriguez, A.: Double chunking for solving svms for very large datasets. In: Proceedings of Learning 2004, Elche, Spain 1 (2004), eprints.pascal-network.org/archive/00001184/01/learn04.pdf
DeJong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD thesis University of Michigan, Ann Arbor (1975)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
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Stoean, R., Preuss, M., Stoean, C., El-Darzi, E., Dumitrescu, D. (2009). An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy. In: Abraham, A., Hassanien, AE., Siarry, P., Engelbrecht, A. (eds) Foundations of Computational Intelligence Volume 3. Studies in Computational Intelligence, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01085-9_11
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DOI: https://doi.org/10.1007/978-3-642-01085-9_11
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