Abstract
This paper proves the problem of losing incremental samples’ information of the present SVM incremental learning algorithm from both theoretic and experimental aspects, and proposes a new incremental learning algorithm with support vector machine based on hyperplane-distance. According to the geometric character of support vector, the algorithm uses Hyperplane-Distance to extract the samples, selects samples which are most likely to become support vector to form the vector set of edge, and conducts the support vector machine training on the vector set. This method reduces the number of training samples and effectively improves training speed of incremental learning. The results of experiment performed on Chinese webpage classification show that this algorithm can reduce the number of training samples effectively and accumulate historical information. The HD-SVM algorithm has higher training speed and better precision of classification.
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References
Vapnik VN (1998) Statistical learning theory. Wiley, New York
Nello C et al (2004) An introduction to support vector machines and other kerner-based learning methods
Xiao R, Wang J-C, Sun Z-X (2002) An incremental svm learning algorithm. J Nan Jing Univ (Natural Sci) 38(2):152–157
Flake GW, Lawrence S (2002) Efficient SVM regression training with SMO. Mach Learn 46:271–290
Syed N, Liu H, Sung K (1999) Incremental learning with support vector machines. In: Proceeding of international joint conference on artificial intelligence (IJCAI). Sweden
Mitra P, Murthy CA, Pal SK (2000) Data condensation in large databases by incremental learning with support vector machines. In: Proceeding of international conference on pattern recognition (ICPR), pp 2708–2711
Domeniconi C, Gunopulos D (2001) Incremental support vector machine construction. In: Proceeding of IEEE international conference on data mining series (ICDM), pp 589–592
Cauwenberghs G, Poggio T (2000) Incremental and decremental support vector machine learning. In: Advances in neural information processing systems
Katagiri S, Abe S (2005) Selecting support vector candidates for incremental training. In: Proceeding of IEEE international conference on systems, man, and cybernetics (SMC), pp 1258–1263
Tax DMJ, Duin RPW (2001) Outliers and data descriptions. In: Proceeding of seventh annual conference of the advanced school for computing and imaging, pp 234–241
Manevitz ML Yousef M (2001) One-class SVMs for document classification, Mach Learn 2:139–154
Debnath R, Takahashi H (2004) An improved working set selection method for SVM decomposition method. In: Proceeding of IEEE international conference intelligence systems, Varna, Bulgaria, pp 21–24, 520–523
Debnath R, Muramatsu M, Takahashi H (2005) An efficient support vector machine learning method with second-order cone programming for large-scale problems. Appl Intell 23:219–239
Zhou W-D, Li Z, Li-Cheng J (2001) An analysis of SVMs generalization performance. Acta Electron Sin 29(5):590–594
Heaton J (2002) Net-robot Java programme guide. Publishing House of Electronics Industry, pp 1–141
Hsu C-W, Lin C-J (2002) A simple decomposition method for support vector machines. Mach Learn 46:291–314
Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
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Li, C., Liu, K. & Wang, H. The incremental learning algorithm with support vector machine based on hyperplane-distance. Appl Intell 34, 19–27 (2011). https://doi.org/10.1007/s10489-009-0176-9
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DOI: https://doi.org/10.1007/s10489-009-0176-9