Moderating the outputs of support vector machine classifiers

IEEE Transactions on Neural Networks

Volumn 10, Issue 5, 1999, Pages 1018-1031

  Kwok, James Tin Yau ^a,b  

^a IEEE

^b HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; VECTORS;

SUPPORT VECTOR MACHINES (SVM);

NEURAL NETWORKS;

EID: 0032594960     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.788642     Document Type: Article

References (45)

1. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 955-974, 1998.
2. V. Cherkassky and F. Mulier, Learning from Data: Concepts, Theory, and Methods. New York: Wiley, 1998.
3. C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, pp. 273-297, 1995.
4. B. Schölkopf, C. Burges, and A. Smola, Advances in Kernel Methods: Support Vector Machines. Cambridge, MA: MIT Press, 1998.
5. A. J. Smola and B. Schölkopf, "A tutorial on support vector regression," NeuroCOLT2, Tech. Rep. NC2-TR-1998-030, 1998.
6. V. Vapnik, The Nature of Statistical Learning Theory. Berlin, Germany: Springer-Verlag, 1995.
7. _, Statistical Learning Theory. New York: Wiley, 1998.
8. T. Joachims, "Text categorization with support vector machines: Learning with many relevant features," in European Conf Machine Learning, 1998.
9. J. T. Kwok, "Automated text categorization using support vector machine," in Proc. Int. Conf. Neural Inform. Processing, Kitakyushu, Japan, Oct. 1998, pp. 347-351.
10. E. Osuna, R. Freund, and F. Girosi, "Training support vector machines: an application to face detection," in Proc. Comput. Vision Pattern Recognition, Puerto Rico, June 1997.
11. B. Schölkopf, "Support vector learning," Ph.D. dissertation, Technische Universität Berlin, Germany, 1997.
12. C. J. C. Burges and B. Schölkopf, "Improving the accuracy and speed of support vector machines," in Advances in Neural Information Processing Systems 9, M. C. Mozer, M. I. Jordan, and T. Petsche, Eds. San Mateo, CA: Morgan Kaufmann, 1996, pp. 375-381.
13. E. Osuna, R. Freund, and F. Girosi, "An improved training algorithm for support vector machines," in NNSP, 1997.
14. B. Schölkopf, C. Burges, and V. Vapnik, "Incorporating invariances in support vector learning machines," in Proc. Int. Conf. Artificial Neural Networks, 1996.
15. H. D. Drucker, C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik, "Support vector regression machines," in Advances in Neural Information Processing Systems 9, M. C. Mozer, M. I. Jordan, and T. Petsche, Eds. San Mateo, CA: Morgan Kaufmann, 1997, pp. 155-161.
16. V. Vapnik, S. E. Golowich, and A. Smola, "Support vector method for function approximation, regression estimation, and signal processing," in Advances in Neural Information Processing Systems 9, M. C. Mozer, M. I. Jordan, and T. Petsche, Eds. San Mateo, CA: Morgan Kaufmann, 1997, pp. 281-287.
17. K. R. Müller, A. J. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik, "Predicting time series with support vector machines," in Proc. Int. Conf. Artificial Neural Networks, 1997.
18. J. Weston, A. Gammerman, M. Stitson, V. Vapnik, V. Vovk, and C. Watkins, "Density estimation using support vector machines," Royal Holloway Univ. London, U.K., Tech. Rep. CSD-TR-97-23, 1998.
19. D. J. C. MacKay, "The evidence framework applied to classification networks," Neural Comput., vol. 4, no. 5, pp. 720-736, Sept. 1992.
20. M. D. Richard and R. P. Lippmann, "Neural network classifiers estimate Bayesian a posteriori probabilities," Neural Comput., vol. 3, pp. 461-483, 1991.
21. T. Hastie and R. Tibshirani, "Classification by pairwise coupling," Stanford Univ. Univ. Toronto, Tech. Rep., 1996.
22. S. Dumais, J. Platt, D. Heckerman, and M. Sahami, "Inductive learning algorithms and representations for text categorization," in Proc. 7th Int. Conf. Inform. Knowledge Management, 1998.
23. J. Platt, 1998, private communication.
24. M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam, and S. Slattery, "Learning to extract symbolic knowledge from the World Wide Web," in Proc. 15th Nat. Conf. Artificial Intell., 1998.
25. A. Smola, B. Schölkopf, and K.-R. Müller, "The connection between regularization operators and support vector kernels," Neural Networks, vol. 11, pp. 637-649, 1998.
26. C. K. I. Williams, "Prediction with Gaussian processes: From linear regression to linear prediction and beyond," in Learning and Inference in Graphical Models, M. I. Jordan, Ed. Boston, MA: Kluwer, 1998.
27. J. T. Kwok, "The evidence framework applied to support vector machine," IEEE Trans. Neural Networks, submitted for publication.
28. _, "Integrating the evidence framework and the support vector machine," in Proc. European Symp. Artificial Neural Networks, Bruges, Belgium, Apr. 1999, pp. 177-182.
29. D. J. C. MacKay, "Bayesian interpolation," Neural Comput., vol. 4, no. 3, pp. 415-447, May 1992.
30. _, "Bayesian model comparison and backprop nets," in Advances in Neural Information Processing Systems 4, J. E. Moody, S. J. Hanson, and R. P. Lippmann, Eds. San Mateo, CA: Morgan Kaufmann, 1992, pp. 839-846.
31. _, "A practical Bayesian framework for backpropagation networks," Neural Comput., vol. 4, no. 3, pp. 448-472, May 1992.
32. _, "Probable networks and plausible predictions - A review of practical Bayesian methods for supervised neural networks," Network: Comput. Neural Syst., vol. 6, no. 3, pp. 469-505, Aug. 1995.
33. H. H. Thodberg, "A review of Bayesian neural networks with an application to near infrared spectroscopy," IEEE Trans. Neural Networks, vol. 7, pp. 56-72, 1996.
34. J. O. Berger, Statistical Decision Theory and Bayesian Analysis, 2nd ed. New York: Springer-Verlag, Springer Series in Statistics, 1985.
35. J. M. Bernardo and A. F. M. Smith, Bayesian Theory. New York: Wiley, 1994.
36. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C, 2nd ed. New York: Cambridge Univ. Press, 1992.
37. C. Blake, E. Keogh, and C. J. Merz, "UCI repository of machine learning databases," Univ. California, Irvine, Dept. Inform. Comput. Sci., 1998. Available http://www.ics.uci.edu/~mlearn/MLRepository.html
38. V. Blanz, B. Schölkopf, H. Bulthoff, C. Burges, V. Vapnik, and T. Vetter, "Comparison of view-based object recognition algorithms using realistic 3-D models," in Proc. Int. Conf. Artificial Neural Networks, 1996.
39. B. Schölkopf, C. Burges, and V. Vapnik, "Extracting support data for a given task," in Proc. 1st Int. Conf. Knowledge Discovery Data Mining, 1995.
40. W. B. Frakes and R. Baeza-Yates, Information Retrieval: Data Structures and Algorithms. Englewood Cliffs, NJ: Prentice-Hall, 1992.
41. D. D. Lewis, "An evaluation of phrasal and clustered representations on a text categorization task," in SIGIR Forum, Copenhagen, Denmark, June 1992, pp. 37-50.
42. C. Apte, F. Damerau, and S. Weiss, "Text mining with decision rules and decision trees," in Proc. Conf. Automat. Learning Discovery, June 1998.
43. Y. Yang, "An evaluation of statistical approaches to text categorization," Dept. Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, CMU-CS 97-127, 1997.
44. R. M. Neal, Bayesian Learning for Neural Networks, Lecture Notes in Statistics. Berlin, Germany: Springer, 1996.
45. C. K. I. Williams, "Computing with infinite networks," in Advances in Neural Information Processing Systems 9, M. C. Mozer, M. I. Jordan, and T. Petsche, Eds. San Mateo, CA: Morgan Kaufmann, 1997, pp. 295-301.