Abstract
In this article, we propose a reformulation of ε-insensitive Support Vector Regression as a classification problem with a priori knowledge in the form of detractors. So we can use the one general solver for Support Vector Machines classification and regression problems. Moreover, we can apply all the applications for a priori knowledge in the form of detractors also for ε-insensitive Support Vector Regression. These are manipulating of the regression function and creating improved reduced models by removing support vectors. Indeed, the experiments show that the new reformulation of Support Vector Regression leads to an effective application of detractors for regression problems. The tests were performed on various regression data sets and on stock price data from public domain repositories.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Karasuyama, M., Takeuchi, I., Nakano, R.: Reducing SVR support vectors by using backward deletion. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part III. LNCS (LNAI), vol. 5179, pp. 76–83. Springer, Heidelberg (2008)
Keerthi, S.S., Chapelle, O., DeCoste, D.: Building support vector machines with reduced classifier complexity. Journal of Machine Learning Research 7, 1493–1515 (2006)
Libsvm data sets, http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/
Lin, F., Guo, J.: A novel support vector machine algorithm for solving nonlinear regression problems based on symmetrical points. In: Proceedings of the 2nd International Conference on Computer Engineering and Technology, pp. 176–180 (2010)
Orchel, M.: Incorporating detractors into SVM classification. In: Cyran, K., Kozielski, S., Peters, J., Stańczyk, U., Wakulicz-Deja, A. (eds.) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol. 59, pp. 361–369. Springer, Heidelberg (2009)
Orchel, M.: Incorporating a priori knowledge from detractor points into support vector classification. In: Dobnikar, A., Lotrič, U., Šter, B. (eds.) ICANNGA 2011, Part II. LNCS, vol. 6594, pp. 332–341. Springer, Heidelberg (2011)
Orchel, M.: Regression based on support vector classification. In: Dobnikar, A., Lotrič, U., Šter, B. (eds.) ICANNGA 2011, Part II. LNCS, vol. 6594, pp. 353–362. Springer, Heidelberg (2011)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimization, pp. 185–208. MIT Press, Cambridge (1999)
Vapnik, V.N.: Statistical Learning Theory. Wiley Interscience, Hoboken (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Orchel, M. (2011). Support Vector Regression as a Classification Problem with a Priori Knowledge in the Form of Detractors. In: Czachórski, T., Kozielski, S., Stańczyk, U. (eds) Man-Machine Interactions 2. Advances in Intelligent and Soft Computing, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23169-8_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-23169-8_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23168-1
Online ISBN: 978-3-642-23169-8
eBook Packages: EngineeringEngineering (R0)