{"title":"Approximation Incremental Training Algorithm Based on a Changeable Training Set","authors":"Yi-Fan Zhu, Wei Zhang, Xuan Zhou, Qun Li, Yong-Lin Lei","volume":57,"journal":"International Journal of Computer and Information Engineering","pagesStart":1026,"pagesEnd":1036,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/5883","abstract":"The quick training algorithms and accurate solution\r\nprocedure for incremental learning aim at improving the efficiency of\r\ntraining of SVR, whereas there are some disadvantages for them, i.e.\r\nthe nonconvergence of the formers for changeable training set and\r\nthe inefficiency of the latter for a massive dataset. In order to handle\r\nthe problems, a new training algorithm for a changeable training\r\nset, named Approximation Incremental Training Algorithm (AITA),\r\nwas proposed. This paper explored the reason of nonconvergence\r\ntheoretically and discussed the realization of AITA, and finally\r\ndemonstrated the benefits of AITA both on precision and efficiency.","references":"[1] V. Vapnik, The Nature of Statistical Learning Theory. New York:\r\nSpringer-Verlag, 1995.\r\n[2] C. J. Burges, \"A tutorial on support vector machines for pattern recognition,\"\r\nData Mining and Knowledge Discovery, vol. 2, pp. 121-167,\r\n1998.\r\n[3] B. Sch\u252c\u00bfolkopf, C. J. Burges, and A. J. Smola, Advances in Kernel\r\nMethods - Support Vector Learning. Cambridge, England: The MIT\r\nPress, 1999.\r\n[4] B. Sch\u252c\u00bfolkopf and A. J. Smola, Learning with Kernels. Cambridge:\r\nMIT Press, 2002.\r\n[5] A. J. Smola and B. Sch\u252c\u00bfolkopf, \"A tutorial on support vector regression,\"\r\nStatistics and Computing, vol. 14, no. 3, pp. 199-222, 2004.\r\n[6] G. Bloch, F. Lauer, G. Colin, and Y. Chamaillard, \"Support vector regression\r\nfrom simulation data and few experimental samples,\" Information\r\nSciences, vol. 178, pp. 3813-3827, 2008.\r\n[7] J.-B. Gao, S. R. Gunn, and C. J. Harris, \"Mean field method for the\r\nsupport vector machine regression,\" Neurocomputing, vol. 50, pp. 391-\r\n405, 2003.\r\n[8] K.-R. M\u252c\u00bfuller, A. J. Smola, G. R\u252c\u00bfatsch, B. Sch\u252c\u00bfolkopf, J. Kohlmorgen,\r\nand V. Vapnik, \"Predicting time series with support vector machines,\"\r\nin Artificial Neural Networks ICANN-97, W. Gerstner, A. Germond,\r\nM. Hasler, and J.-D. Nicoud, Eds., vol. 1327. Berlin: Springer Lecture\r\nNotes in Computer Science, 1997, pp. 999-1004.\r\n[9] D. Odapally, \"Structural optimization using femlab and smooth support\r\nvector regression,\" Ph.D. dissertation, University of Texas, 2006.\r\n[10] E. E. Osuna, R. Freund, and F. Girosi, \"Training support vector\r\nmachines: An application to face detection,\" in IEEE Conference on\r\nComputer Vision and Pattern Recognition, 1997, pp. 130-136.\r\n[11] J. C. Platt, \"Fast training of support vector machines using sequential\r\nminimal optimization,\" in Advances in Kernel Methods-Support Vector\r\nLearning, B. Sch?lkopf, C. J. Burges, and A. J. Smola, Eds. Cambridge,\r\nEngland: MIT Press, 1999.\r\n[12] W. Zhou, \"Kernel-based learning machines,\" Ph.D. dissertation, Xi-an\r\nElectronic and Science University, 2003.\r\n[13] G. Cauwenberghs and T. Poggio, \"Incremental and decremental support\r\nvector machine learning,\" Machine Learning, vol. 44, no. 13, pp. 409-\r\n415, 2001.\r\n[14] J. Ma, J. Theiler, and S. Perkins, \"Accurate online support vector\r\nregression,\" Neural Computation, vol. 15, no. 11, pp. 2683-2703, 2003.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 57, 2011"}