Abstract
Ordinal regression is complementary to the standard machine learning tasks of classification and metric regression which goal is to predict variables of ordinal scale. However, every input must be exactly assigned to one of these classes without any uncertainty in standard ordinal regression models. Based on structural risk minimization (SRM) principle, a new support vector learning technique for ordinal regression is proposed, which is able to deal with training data with uncertainty. Firstly, the meaning of the uncertainty is defined. Based on this meaning of uncertainty, two algorithms have been derived. This technique extends the application horizon of ordinal regression greatly. Moreover, the problem about early warning of food security in China is solved by our algorithm.
This paper is sponsored by China National Science Foundation under grant No. 90412009.
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References
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernelbased Learning Methods. Cambridge Univerdity Press, Cambridge (2000)
Herbrich, R., Graepel, T., Obermayer, K.: Large Margin Rank Boundaries for Ordinal Regression. In: Advances in Large Margin Classifiers, pp. 115–132 (2000)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel based Learning Methods. Cambridge University Press, Cambridge (2000)
Herbrich, R.: Learning Kernel Classifiers. MIT Press, Cambridge (2002)
Lin, C.-F., Wang, S.-D.: Fuzzy Support Vector Machines. IEEE Transactions on Neural Networks 13(2) (2002)
Li, Z., Zhao, Z., Wu, Y.: The Analysis on Early Warning of Food Security in China. China Agriculture Econonmy (in Chinese) 1 (1998)
An, X.: Theory, Method and System Design on Early Warning of Food Security. World Agriculture (in Chinese) 231 (1998)
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© 2005 Springer-Verlag Berlin Heidelberg
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Guangli, L., Ruizhi, S., Wanlin, G. (2005). Uncertainty Support Vector Method for Ordinal Regression. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_81
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DOI: https://doi.org/10.1007/11539087_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
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