HANDWRITTEN DIGIT RECOGNITION BASED ON A NEURAL – SVM COMBINATION

H. Nemmour and Y. Chibani

References

  1. [1] K.I. Kim, K. Jung, S.H. Park, & H.J. Kim, Support vector machines for texture classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(11), 2002, 1542– 1550.
  2. [2] L. Yang, C.Y. Suen, T.D. Bui, & P. Zhang, Discrimination of similar handwritten numerals based on invariant curvature features, Pattern Recognition Journal, 38(7), 2005, 947–963.
  3. [3] D. Keysers, W. Mcherey, & H. Ney, Adaptation in statistical pattern recognition using tangent vectors, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2), 2004, 269–274.
  4. [4] V. Vapnik, The nature of learning theory (New York: Springer Verlag, 1995).
  5. [5] C.J.C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2, 1998, 121–167.
  6. [6] J.C. Platt, N. Cristianini, & J. Shawe-Taylor, Large margin DAGs for multiclass classification, MIT Press (Advances in Neural Information Processing Systems), 12, 2000, 547–553.
  7. [7] R. Collobert, Y. Bengio, & S. Bengio, Scaling large learning problems with hard parallel mixture, International Journal of Pattern Recognition and Artificial Intelligence, 17(3), 2003, 349–365.
  8. [8] H. Nemmour & Y. Chibani, Fuzzy integral based-mixture to speed up the one-against-all multiclass SVMs, Proc. 31th Int. Conf. on Speech and Signal Processing, Toulouse, 2006, 697–700.
  9. [9] S. Tian, S. MU, & C. Yin, Sequence-similarity kernels for SVMs to detect anomalies in system calls, Neuro Computing Journal, 70(4–6), 2007, 859–866.
  10. [10] N.E. Ayat, M. Cheriet, L. Remaki, & C.Y. Suen, KMOD – a new support vector machine kernel with moderate decreasing for pattern recognition. Application to digit image recognition, Proc. 6th Int. Conf. on Document Analysis and Recognition, Washington, 2001, 1215–1219.
  11. [11] A. Pozdnoukhov & S. Bengio, Tangent vector kernels for invariant image classification with SVMs, Proc. 17th Int. Conf. on Pattern Recognition Combridge, 2004, 486–489.
  12. [12] D. DeCoste & B. Schölkopf, Training invariant support vector machines, Machine Learning, 46, 2002, 161–190.
  13. [13] P. Simard, Y. Le Cun, J. Denker, & B. Victorri, Efficient pattern recognition using a new transformation distance, Advances in Neural Information Processing Systems, 5, 1993, 50–58.
  14. [14] D. Trier, A.K. Jain, & T. Taxt, Feature extraction methods for character recognition: A survey, Pattern Recognition Journal, 29(4), 1996, 641–662.
  15. [15] J.C. Platt, Fast training of support vector machines using sequential minimal optimization, MIT Press (Advances in Kernel Methods-Support Vector Machines), 12, 1999, 185–208.
  16. [16] H.P. Huang & Y.H. Liu, Fuzzy support vector machines for pattern recognition and data mining, International Journal of Fuzzy Systems, 4(3), 2002, 826–835.
  17. [17] R. Plamondon & S.N. Srihari, On-line and off-line handwriting recognition: A comprehensive survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 2000, 63–84.
  18. [18] D.E. Rumlhart, G.E. Hinton, & R.J. Williams, Learning internal representations by error propagation, MIT Press (Parallel Distributed Processing: Explorations in the Microstructure of Cognition), 1, 1986, 318–362.
  19. [19] B. Haasdonk & D. Keysers, Tangent distance kernels for support vector machines, Proc. 16th Int. Conf. on Pattern Recognition, Montreal, 2002, 864–868.
  20. [20] P. Simard, Y. Le Cun, J. Denker, & B. Victorri, Transformation invariance in pattern recognition – tangent distance and tangent propagation, Lecture Notes on Computer Science, 1524, 1998, 239–274.
  21. [21] B. Schölkopf, Support vector learning, Doctoral Dissertation, University of Berlin, Berlin, 1997.

Important Links:

Go Back