Abstract
Support vector machines (SVMs), introduced by Vapnik in the early 1990’s, are powerful techniques for machine learning and data mining. Recent breakthroughs have led to advancements in the theory and applications. SVMs were developed to solve the classification problem at first, but they have been extended to the domain of regression, clustering problems. Such standard SVMs require the solution of either a quadratic or a linear programming. In this chapter, we introduced the basic concepts of SVMs: method of maximum margin, dual problem, soft margin, kernel functions, and then presented the standard algorithm C-support vector classification (C-SVC). Especially, considering the classification problem of which the training set with nominal attributes, we built a new SVM which can learn the distance of the nominal attribute values, to improve most popular approaches assuming that all attribute values are of equal distance from each other.
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References
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. University of California, Irvine. http://www.ics.uci.edu/~mlearn/MLRepository.html (1998)
Boser, B., Guyon, I., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Fifth Annual Workshop on Computational Learning Theory, pp. 144–152 (1992)
Deng, N.Y., Tian, Y.J.: The New Approach in Data Mining—Support Vector Machines. Science Press, Beijing (2004)
Schölkopf, B., Smola, A.J.: Learning with Kernels—Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)
Tian, Y.J.: Support vector regression machine and its application. Ph.D. Thesis, China Agricultural University (2005)
Tian, Y.J., Deng, N.Y.: Support vector classification with nominal attributes. In: Proceedings of 2005 International Conference on Computational Intelligence and Security, Xi’an, pp. 586–591 (2005)
Tsang, I.W., Kwok, J.T.: Distance metric learning with kernels. In: Proceedings of the International Conference on Artificial Neural Networks, Istanbul, Turkey (2003)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Vapnik, V.N., Chapelle, O.: Bounds on error expectation for SVM. In: Advances in Large-Margin Classifiers (Neural Information Processing), pp. 261–280. MIT Press, Cambridge (2000)
Zhao, K., Tian, Y.J., Deng, N.Y.: Unsupervised and semi-supervised two-class support vector machines. In: Proceedings of the 7th IEEE ICDM 2006 Workshop, pp. 813–817 (2006)
Zhao, K., Tian, Y.J., Deng, N.Y.: Unsupervised and semi-supervised Lagrangian support vector machines. In: Proceedings of the 7th International Conference on Computational Science, pp. 882–889 (2007)
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Shi, Y., Tian, Y., Kou, G., Peng, Y., Li, J. (2011). Support Vector Machines for Classification Problems. In: Optimization Based Data Mining: Theory and Applications. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-0-85729-504-0_1
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DOI: https://doi.org/10.1007/978-0-85729-504-0_1
Publisher Name: Springer, London
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