Support vector pursuit learning

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn 6, Issue , 2004, Pages 5841-5846

  Liu, Yangguang ^a   He, Qinming ^a   Tang, Yongchuan ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Incremental batch learning; Regression; Support Vector Machines

Indexed keywords

INCREMENTAL BATCH LEARNING; LINEAR REGRESSION; SCALAR PRODUCT; SUPPORT VECTOR MACHINES (SVM);

ALGORITHMS; APPROXIMATION THEORY; OPTIMIZATION; PROBLEM SOLVING; REGRESSION ANALYSIS; SET THEORY;

LEARNING SYSTEMS;

EID: 15744367513     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2004.1401127     Document Type: Conference Paper

References (18)

1. R. Collobert and S. Bengio, . "SVMTorch: Support vector machines for large-scale regression problems", Journal of Machine Learning Research, Vol. 1, pp. 143-160, 2001.
2. L. Csato and M. Opper, " Sparse representation for gaussian process models". In Leen, T. K.; Dietterich, T. G.; and Tresp, V., eds., Advances in Neural Information Processing Systems, Vol. 13, pp. 444-450. Cambridge, MA: MIT Press, 2001.
3. C. Domeniconi and D. Gunopulos, "Incremental support vector machine construction". In Nick Cercone, Tsau Young Lin, X.W., ed., Proceedings of the 2001 IEEE International Conference on Data Mining, pp. 589-592. San Jose, California, USA: IEEE Computer Society, 2001.
4. Y. Engel, S. Manner and R. Meir, "Sparse online greedy support vector regression". In Elomaa, T., and ann Hannu Toivonen, H. M., eds., ECML, Vol. 2430 of Lecture Notes in Computer Science, pp. 84-96, Springer, 2002.
5. G. Flake and S. Lawrence, "Efficient svm regression training with smo". Machine Learning, Vol. 46, pp. 271-290, 2002.
6. C. Gentile, "A new approximate maximal margin classification algorithm". Journal of Machine Learning Research, Vol. 2, pp. 213-242, 2001.
7. Graepel, T. R. Herbrich and R. C. Williamson, "From margin to sparsity". In Leen, T. K.; Dietterich, T. G.; and Tresp, V., eds., Advances in Neural Information Processing Systems, Vol. 13, pp. 210-216. Cambridge, MA: MIT Press, 2001.
8. M. Herbster," Learning additive models online with fast evaluating kernels". In Proceedings of 14th Annual Conference on Computational Learning Theory (COLT), pp. 444-460. Springer, 2001.
9. T. Joachims, " Making large-sclae support vedtor machine learning practical". In Scholkopf, B.; Burges, C.; and Smola, A., eds., Advances in Kernel Methods. MIT Press, 1999.
10. J. Kivinen, A. J. Smola and R. C. Willianmson, "Online learning with kernels". In Dietterich, T. G.; Becker, S.; and Ghahramani, Z., eds., Advances in Neural Information Processing Systems, pp. 785-792, 2001.
11. Y. Li and P. Long, "The relaxed online maximum margin algorithm". In Solla, A.; Leen, T. K.; and Mller, K.-R., eds., Advances in Neural Information Processing Systems, Vol. 12, pp. 498-504. Cambridge, MA: MIT Press, 2001.
12. J. Ma, J. Theiler and S. Perkins, "Accurate online support vector regression". Neural Computation, Vol.15 No. 11, pp. 2683-2703, 2003.
13. E. Osuna, R. Freund and F. Girosi, "An improved training algorithm for support vector machines". In Proc. Of IEEE NNSP'97, 1997.
14. F. Provost and V. Kolluri, "A survey of methods for scaling up inductive algorithms". Data Mining and Knowledge Discovery, Vol.3, No. 2, pp. 131-169, 1999.
15. L. Ralaivola and Buc. F.d'Alche, "Incremental support vector machine learning: a local approach". In Proceedings of International Conference on Artificial Neural Networks, pp. 21-25, 2001.
16. N. A. Syed, H. Liu and K. Sung," Incremental learning with support vector machines". In Proceedings of the Workshop on Support Vector Machines at the international Joint Conference on Artificial Intelligence (IJCAI-99), 1999.
17. V. Vapnik, "The Nature of Statistical Learning Theory". Springer-Verlag, New York, 1995.
18. V. Vapnik, "Statistical Learning Theory". Wiley, New York, 1998.