Fast Support Vector Machines for Continuous Data

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 39, Issue 4, 2009, Pages 989-1001

  Kramer, Kurt A ^a   Hall, Lawrence O ^a   Goldgof, Dmitry B ^a   Remsen, Andrew ^b   Luo, Tong ^c  

^a University of South Florida   (United States)

^b UNIVERSITY OF SOUTH FLORIDA   (United States)

^c Searchspark   (United States)

Author keywords

Compression; data squashing; speedup; support vector machines (SVMs)

Indexed keywords

EID: 85008065094     PISSN: 10834419     EISSN: 19410492     Source Type: Journal    
DOI: 10.1109/TSMCB.2008.2011645     Document Type: Article

References (45)

1. S. Asharaf and M. N. Murty, “Scalable non-linear support vector machine using hierarchical clustering,” in Proc. Int. Conf. Pattern Recog., 2006, vol. 1, pp. 908–911.
2. S. Asharaf, M. N. Murty, and S. K. Shevade, “Multiclass core vector machine,” in Proc. ICML, 2007, pp. 41–48.
3. D. Boley and D. Cao, “Training support vector machines using adaptive clustering,” in Proc. SIAM Int. Conf. Data Mining, 2004, pp. 126–137.
4. C. J. C. Burges, “Simplified support vector decision rules,” in Proc. Int. Conf. Mach. Learn., 1996, pp. 71–77.
5. C. J. C. Burges and B. Scholkopf, “Improving the accuracy and speed of support vector machines,” in Proc. Adv. Neural Inform. Process. Syst., 1997, vol. 9, pp. 375–381.
6. L. Cao, S. Keerthi, C.-J. Ong, J. Zhang, U. Periyathamby, X.-J. Fu, and H. Lee, “Parallel sequential minimal optimization for the training of support vector machines,” IEEE Trans. Neural Netw., vol. 17, no. 4, pp. 1039–1049, Jul. 2006.
7. C. Chang and C. Lin, LIBSVM: A Library for Support Vector Machines (version 2.3), 2001.
8. W. G. Cochran, Sampling Techniques, 3rd ed. New York: Wiley, 1977.
9. R. Collobert, Y. Bengio, and S. Bengio, “Scaling large learning problems with hard parallel mixtures,” Int. J. Pattern Recogn. Artif. Intell., vol. 17, no. 3, pp. 349–365, 2003.
10. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 2nd ed. Cambridge, MA: MIT Press, 2001.
11. T. G. Dietterich, “Approximate statistical tests for comparing supervised classification learning algorithms,” Neural Comput., vol. 10, no. 7, pp. 1895–1924, Oct. 1998.
12. J. X. Dong and A. Krzyzak, “A fast SVM training algorithm,” Int. J. Pattern Recogn. Artif. Intell., vol. 17, no. 3, pp. 367–384, 2003.
13. W. DuMouchel, C. Volinsky, T. Johnson, C. Cortes, and D. Pregibon, “Squashing flat files flatter,” in Proc. Knowl. Discov. Data Mining, 1999, pp. 6–15.
14. S. Eschrich, J. Ke, L. Hall, and D. Goldgof, “Fast accurate fuzzy clustering through data reduction,” IEEE Trans. Fuzzy Syst., vol. 11, no. 2, pp. 262–270, Apr. 2003.
15. S. Fine and K. Scheinberg, “Efficient SVM training using low-rank kernel representations,” J. Mach. Learn. Res., vol. 2, no. 2, pp. 243–264, Mar. 2001.
16. Jayadeva, R. Khemchandani, and S. Chandra, “Twin support vector machines for pattern classification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 5, pp. 905–910, May 2007.
17. T. Joachims, “Making large-scale support vector machine learning practical,” in Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT Press, 1999, pp. 169–184.
18. S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy, “Improvements to Platt's SMO algorithm for SVM design,” Neural Comput., vol. 13, no. 3, pp. 637–649, 2001.
19. Y. Liu, Z. You, and L. Cao, “A novel and quick SVM-based multiclass classifier,” Pattern Recognit., vol. 39, no. 11, pp. 2258–2264, Nov. 2006.
20. T. Luo, L. O. Hall, D. B. Goldgof, and A. Remsen, “Bit reduction support vector machine,” in Proc. 5th IEEE ICDM, 2005, pp. 733–736.
21. T. Luo, K. Kramer, D. Goldgof, L. Hall, S. Samson, A. Remsen, and T. Hopkins, “Active learning to recognize multiple types of plankton,” in Proc. 17th Conf. Int. Assoc. Pattern Recognit., 2004, vol. 3, pp. 478–481.
22. T. Luo, K. Kramer, D. Goldgof, L. Hall, S. Samson, A. Remsen, and T. Hopkins, “Recognizing plankton images from the shadow image particle profiling evaluation recorder,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 4, pp. 1753–1762, Aug. 2004.
23. D. Madigan, N. Raghavan, W. Dumouchel, M. Nason, C. Posse, and G. Ridgeway, “Likelihood-based data squashing: A modeling approach to instance construction,” Data Mining Knowl. Discov., vol. 6, no. 2, pp. 173–190, Apr. 2002.
24. O. L. Mangasarian and D. R. Musicant, “Lagrangian support vector machines,” J. Mach. Learn. Res., vol. 1, pp. 161–177, Sep. 2001.
25. C. J. Merz and P. M. Murphy, UCI repository of machine learning database, 1999. [Online]. Available: http://www.ics.uci.edu/mlearn/MLRepository.html
26. D. Michie, D. J. Spiegelhalter, and C. C. Taylor, Machine Learning, Neural and Statistical Classification. Englewood Cliffs, NJ: Prentice-Hall, 1994.
27. E. Osuna and F. Girosi, “Reducing the run-time complexity in support vector machines,” in Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT Press, 1999, pp. 271–283.
28. A. Owen, “Data squashing by empirical likelihood,” Data Mining Knowl. Discov., vol. 7, no. 1, pp. 101–113, Jan. 2003.
29. D. Pavlov, D. Chudova, and P. Smyth, “Towards scalable support vector machines using squashing,” in Proc. 6th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2000, pp. 295–299.
30. D. Pavlov, J. Mao, and B. Dom, “Scaling-up support vector machines using boosting algorithm,” in Proc. 15th Int. Conf. Pattern Recog., 2000, vol. 2, pp. 219–222.
31. J. Platt, “Fast training of support vector machines using sequential minimal optimization,” in Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT Press, 1999, pp. 185–208.
32. G. Ratsch, T. Onoda, and K. Muller, “Soft margins for AdaBoost,” Mach. Learn., vol. 42, no. 3, pp. 287–320, Mar. 2001.
33. B. Scholkopf, S. Kah-Kay, C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik, “Comparing support vector machines with Gaussian kernels to radialbasis function classifiers,” IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2758–2765, Nov. 1997.
34. B. Scholkopf, S. Mika, C. Burges, P. Knirsch, K. R. Muller, G. Ratsch, and A. Smola, “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 1000–1017, Sep. 1999.
35. B. Scholkopf and A. J. Smola, Learning With Kernels. Cambridge, MA: MIT Press, 2002.
36. Y. C. L. Shih, J. D. M. Rennie, and D. R. Karger, “Text bundling: Statistics based data-reduction,” in Proc. 20th Int. Conf. Mach. Learn., 2003, pp. 696–703.
37. B.-Y. Sun, D.-S. Huang, and H.-T. Fang, “Lidar signal denoising using least-squares support vector machine,” IEEE Signal Process. Lett., vol. 12, no. 2, pp. 101–104, Feb. 2005.
38. K.-K. Sung and T. Poggio, “Example-based learning for view-based human face detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 1, pp. 39–51, Jan. 1998.
39. I. W. Tsang, J. T. Kwok, and P.-M. Cheung, “Core vector machines: Fast SVM training on very large data sets,” J. Mach. Learn. Res., vol. 6, pp. 363–392, Dec. 2005.
40. V. Vapnik, Estimation of Dependences Based on Empirical Data. New York: Springer-Verlag, 2001.
41. 1-SVMs in primal space,” Neurocomputing, vol. 70, no. 7–9, pp. 1554–1560, Mar. 2007.
42. C. K. I. Williams and M. Seeger, “Using the nystrom method to speed up kernel machines,” in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2001, pp. 682–688.
43. G. Wu, E. Chang, Y.-K. Chen, and C. Hughes, “Incremental approximate matrix factorization for speeding up support vector machines,” in Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2006, pp. 760–766.
44. H. Yu, J. Yang, and J. Han, “Classifying large data sets using svm with hierarchical clusters,” in Proc. 9th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2003, pp. 306–315.
45. J. Zhu and T. Hastie, Kernel Logistic Regression and the Import Vector Machine, 2001.