Abstract
We present an iterative preprocessing approach for training a support vector machine for a large dataset, based on balancing the center of mass of input data within a variable margin about the hyperplane. At each iteration, the input data is projected on the hyperplane, and the imbalance of the center of mass for different classes within a variable margin is used to update the direction of the hyperplane within the feature space. The approach provides an estimate for the margin and the regularization constant. In the case of fuzzy membership of the data, the membership function of the input data is used to determine center of mass and to count data points which violate the margin. An extension of this approach to non-linear SVM is suggested based on dimension estimation of the feature space represented via a set of orthonormal feature vectors.
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References
Vapnik, V.N.: An Overview of Statistical Learning Theory. IEEE Transactions on Neural Networks 10(5), 988–999 (1999)
Lin, C.F., Wang, S.D.: Fuzzy Support Vector Machines. IEEE Transactions on Neural Networks 13(2), 464–471 (2002)
Platt, J.: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Technical Report MSR-TR-98-14, Microsoft Research (1998)
Vapnik, V.: Estimation of Dependences Based on Empirical Data. Springer (1982)
Osuna, E., Freund, R., Girosi, F.: An Improved Training Algorithm for Support Vector Machines. In: Proc. IEEE NNSP 1997 (1997)
Cervantes, J., Li, X., Yu, W.: Support Vector Machine Classification Based on Fuzzy Clustering for Large Data Sets. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 572–582. Springer, Heidelberg (2006)
Ben-Hur, A., Weston, J.: A User’s Guide to Support Vector Machines. In: Data Mining Techniques for the Life Sciences, pp. 223–239. Humana Press (2010)
Zanni, L., Serafini, T., Zanghirati, G.: Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems. The Journal of Machine Learning Research 7, 1467–1492 (2006)
Cotter, A., Srebro, N., Keshet, J.: A GPU-Tailored Approach for Training Kernelized SVMs. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 805–813. ACM (August 2011)
Giesen, J., Laue, S., Mueller, J.K.: Basis Expansions for Support Vector Machines, http://cgl.uni-jena.de/pub/Publications/WebHome/CGL-TR-49.pdf
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Tadayon, S., Tadayon, B. (2014). Preprocessing Method for Support Vector Machines Based on Center of Mass. In: Jamshidi, M., Kreinovich, V., Kacprzyk, J. (eds) Advance Trends in Soft Computing. Studies in Fuzziness and Soft Computing, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-319-03674-8_43
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DOI: https://doi.org/10.1007/978-3-319-03674-8_43
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03673-1
Online ISBN: 978-3-319-03674-8
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