Abstract
Support Vector Machine (SVM) is one of the fastest growing methods of machine learning due to its good generalization ability and good convergence performance; it has been successfully applied in various fields, such as text classification, statistics, pattern recognition, and image processing. However, for real-time data collection systems, the traditional SVM methods could not perform well. In particular, they cannot well cope with the increasing new samples. In this paper, we give a survey on online SVM. Firstly, the description of SVM is introduced, then the brief summary of online SVM is given, and finally the research and development of online SVM are presented.
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References
Burges, J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc., 121-167 (1998)
Schölkopf, B.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press (2002)
Vapnik, N.V.: The Nature of Statistical Learning. Springer, New York (1995)
Vapnik, N.V., Lerner, A.: Pattern recognition using generalized portrait method. Automat. Rem. Contr+. 24, 774–780 (1963)
Vapnik, N.V., Chervonenkis A.: A note on one class of perceptrons. Automat. Rem. Contr+. 25 (1964)
Cristianini, N., Shawe Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press (2000)
Dumais, S., Platt, J., Heckerman, D., Sahami, M.: Inductive learning algorithms and representations for text categorization. In: 7th International Conference on Information and Knowledge Management, ACM-CIKM 1998 (1998)
Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: International Conference on Computer Vision and Pattern Recognition, CVPR 1997 (1997)
Schölkopf, B., Smola, A., Muller, K.R.: Kernel Principal Component Analysis. In: Schölkopf, B., Burges, C.J.C., Smola, A. (eds.) Advances in Kernel Methods-Support Vector Learning, pp. 327–352. MIT Press, Cambridge (1999)
Ahmad, A, Khalid, M., Yusof, R.: Kernel methods and support vector machines for handwriting recognition. In: IEEE Student Conference on Research and Development Proceedings (SCOReD 2002), pp. 309-312 (2002)
Cortes, C., Vapnik, N.V.: Support vector networks. Mach. Learn. 20, 273–297 (1995)
Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K.: Improvements to the SMO algorithm for SVM regression. IEEE Trans. Neural Netw. 11(5), 1188–1193 (2000)
Chen, P., Fan, R., Lin, C.: A study on SMO-type decomposition methods for support vector machines. IEEE Trans. Neural Netw. 17(4), 893–908 (2006)
Cai, F., Cherkassky, V.: Generalized SMO algorithm for SVM-based multitask learning. IEEE Trans. Neural Netw. Learn. Syst. 23(6), 997–1003 (2012)
Cortes, C., Vapnik, V.: Support Vector Networks. Mach. Learn. 20, 273–297 (1995)
Bordes, A., Bottou, L., Gallinari, P.: SGD-QN: Careful quasi-Newton stochastic gradient descent. J. Mach. Learn. 10, 1737–1754 (2009)
Souza, R.C., Leite, S.C., Borges, C.C., Neto, R.F.: Online algorithm based on support vectors for orthogonal regression. Pattern Recognition Letters 34(12), 1394–1404 (2013)
Kivinen, J., Smola, A.J., Williamson, R.C.: Online learning with Kernels. IEEE Transactions on Signal Processing, 2165-2176 (2004)
Syed, N., Liu, H., Sung, K.: Handling concept drifts in incremental learning with support vector machines. In: 5th ACM SIGKDD International Conference Knowledge Discovery Data Mining, pp. 317–321 (1999)
Singh, R., Vatsa, M., Ross, A., Noore, A.: Biometric classifier update using online learning: A case study in near infrared face verification. Image Vis. Comput. 28(7), 1098–1105 (2010)
Wang, M., Zhou, X., Li, F., Huckins, J., King, R., Wong, S.T.C.: Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy. Bioinformatics. 24(1), 94–101 (2008)
Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: Dietterich, T.G., Leen, T.K., Tresp, V. (eds.) Advances in Neural Infomation Processing Systems, vol.13, pp. 409-415. MIT Press (2001)
Martin, M.: On-Line support vector machine regression. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 282–294. Springer, Heidelberg (2002)
Bordes, A., Bottou, L.: The Huller: a simple and efficient online SVM. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 505–512. Springer, Heidelberg (2005)
Wang, D.I.: Online Support Vector Machine Based on Convex Hull Vertices Selection. IEEE Transactions on Neural Networks Learning Systems 24(4), 593–608 (2013)
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Zhou, X., Zhang, X., Wang, B. (2016). Online Support Vector Machine: A Survey. In: Kim, J., Geem, Z. (eds) Harmony Search Algorithm. Advances in Intelligent Systems and Computing, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47926-1_26
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DOI: https://doi.org/10.1007/978-3-662-47926-1_26
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