Multiple incremental decremental learning of support vector machines

IEEE Transactions on Neural Networks

Volumn 21, Issue 7, 2010, Pages 1048-1059

  Karasuyama, Masayuki ^a   Takeuchi, Ichiro ^a  

^a NAGOYA INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Incremental decremental algorithm; path following; support vector machines (SVM)

Indexed keywords

COMPUTATIONAL COSTS; DATA POINTS; DECREMENTAL ALGORITHMS; MULTIPLE DATA; ONLINE LEARNING; PATH FOLLOWING;

ALGORITHMS; COST BENEFIT ANALYSIS; COST REDUCTION; VECTORS;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; HUMAN; INFORMATION RETRIEVAL; LEARNING; METHODOLOGY; ONLINE SYSTEM;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; HUMANS; INFORMATION STORAGE AND RETRIEVAL; LEARNING; ONLINE SYSTEMS; PATTERN RECOGNITION, AUTOMATED;

EID: 77954563782     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2010.2048039     Document Type: Article

References (30)

1. E. L. Allgower and K. Georg, "Continuation and path following," Acta Numerica, vol.2, pp. 1-64, Jan. 1993.
2. F. Bach, D. Heckerman, and E. Horvitz, "Considering cost asymmetry in learning classifiers," J. Mach. Learning Res., vol.7, pp. 1713-1741, Aug. 2006.
3. L. Bottou and C.-J. Lin, "Support vector machine solvers," in Large Scale Kernel Machines, L. Bottou, O. Chapelle, D. DeCoste, and J. Weston, Eds. Cambridge, MA: MIT Press, 2007, pp. 301-320.
4. G. Cauwenberghs and T. Poggio, "Incremental and decremental support vector machine learning," in Advances in Neural Information Processing Systems (NIPS 2000), T. K. Leen, T. G. Dietterich, and V. Tresp, Eds., vol.13. Cambridge, MA: MIT Press, 2001, pp. 409-415.
5. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," 2001 [Online]. Available: http://www.csie.ntu.edu.tw/ cjlin/libsvm
6. B.-J. Chen, M.-W. Chang, and C.-J. Lin, "Load forecasting using support vector machines: A study on eunite competition 2001," IEEE Trans. Power Syst., vol.19, no.4, pp. 1821-1830, Nov. 2004.
7. D. DeCoste and K. Wagstaff, "Alpha seeding for support vector machines," in Proc. 6th Assoc. Comput. Mach. Int. Conf. Knowl. Discovery Data Mining (SGKDD), 2000, pp. 345-349.
8. T. Gal, Postoptimal Analysis, Parametric Programming, and Related Topics. Berlin, New York: Walter de Gruyter, 1995.
9. G. H. Golub and C. F. Van Loan, Matrix Computations. Baltimore, MD: Johns Hopkins University Press, 1996.
10. L. Gunter and J. Zhu, "Efficient computation and model selection for the support vector regression," Neural Comput., vol.19, no.6, pp. 1633-1655, 2007.
11. T. Hastie, S. Rosset, R. Tibshirani, and J. Zhu, "The entire regularization path for the support vector machine," J. Mach. Learning Res., vol.5, pp. 1391-1415, Oct. 2004.
12. R. Hyndman, "Time series data library," [Online]. Available: http://www. robhyndman.info/TSDL
13. M. Karasuyama, I. Takeuchi, and R. Nakano, "Efficient leave-m-out cross-validation of support vector regression by generalizing decremental algorithm," New Generation Comput., vol.27, no.4, pp. 307-318, 2009.
14. K.-J. Kim, "Financial time series forecasting using support vector machines," Neurocomputing, vol.55, nos. 1-2, pp. 307-319, 2003.
15. P. Laskov, C. Gehl, S. Kruger, and K.-R. Muller, "Incremental support vector learning: Analysis, implementation and applications," J. Mach. Learning Res., vol.7, pp. 1909-1936, Dec. 2006.
16. M. M. Lee, S. S. Keerthi, C. J. Ong, and D. DeCoste, "An efficient method for computing leave-one-out error in support vector machines," IEEE Trans. Neural Netw., vol.15, no.3, pp. 750-757, May 2004.
17. J. Ma and J. Theiler, "Accurate online support vector regression," Neural Comput., vol.15, no.11, pp. 2683-2703, 2003.
18. M. Martin, "On-line support vector machines for function approximation," Software Dept., Univ. Politecnica Catalunya, Barcelona, Spain, Tech. Rep. LSI-02-11-R, 2002.
19. M. Meyer, "Statlib" [Online]. Available: http://lib.stat.cmu. edu/index. php
20. K.-R. Müller, A. J. Smola, G. Rätsch, B. Schökopf, J. Kohlmorgen, and V. Vapnik, "Using support vector machines for time series prediction," in Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT Press, 1999, pp. 243-253.
21. E. N. Pistikopoulos, M. C. Georgiadis, and V. Dua, Process Systems Engineering: Volume 1: Multi-Parametric Programming. New York: Wiley-VCH, 2007.
22. J. C. Platt, "Fast training of support vector machines using sequential minimal optimization," in Advances in Kernel Methods: Support Vector Learning, B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds. Cambridge, MA: MIT Press, 1999, pp. 185-208.
23. K. Scheinberg, "An efficient implementation of an active set method for SVMs," J. Mach. Learning Res., vol.7, pp. 2237-2257, Dec. 2006.
24. J. R. Schott, Matrix Analysis for Statistics. New York: Wiley- Interscience, 2005.
25. A. Shilton, M. Palaniswami, D. Ralph, and A. Tsoi, "Incremental training of support vector machines," IEEE Trans. Neural Netw., vol.16, no.1, pp. 114-131, Jan. 2005.
26. I. Takeuchi, K. Nomura, and T. Kanamori, "Nonparametric conditional density estimation using piecewise-linear solution path of kernel quantile regression," Neural Comput., vol.21, no.2, pp. 533-559, 2009.
27. F. Tay and L. Cao, "Application of support vector machines in financial time series forecasting," Omega, vol.29, no.4, pp. 309-317, 2001.
28. V. N. Vapnik, The Nature of Statistical Learning Theory. Berlin, Germany: Springer-Verlag, 1995.
29. S. Vishwanathan, A. Smola, and M. Murty, "Simple SVM," in Proc. 20th Int. Conf. Mach. Learning (ICML), 2003, pp. 760-767.
30. G. Wang, D.-Y. Yeung, and F. H. Lochovsky, "A new solution path algorithm in support vector regression," IEEE Trans. Neural Netw., vol.19, no.10, pp. 1753-1767, Oct. 2008.