A Simple Decomposition Method for Support Vector Machines

• Blake, C. L. & Merz, C. J. (1998). UCI repository of machine learning databases. Irvine, CA. (Available at http://www.ics.uci.edu/~mlearn/mLRepository.html)

• Chang, C.-C., Hsu, C.-W., & Lin, C.-J. (2000). The analysis of decomposition methods for support vector machines. IEEE Trans. Neural Networks, 11:4, 1003-1008.

  Google Scholar 

• Cristianini, N. & Shawe-Taylor, J. (2000). An introduction to support vector machines. Cambridge, UK: Cambridge University Press.

  Google Scholar 

• Fletcher, R. (1987). Practical methods of optimization. New York: John Wiley and Sons.

  Google Scholar 

• Friess, T.-T., Cristianini, N., & Campbell, C. (1998). The kernel adatron algorithm: A fast and simple learning procedure for support vector machines. In Proceeding of 15th Intl. Conf. Machine Learning. San Francisco, CA: Morgan Kaufman Publishers.

  Google Scholar 

• Ho, T. K. & Kleinberg, E. M. (1996). Building projectable classifiers of arbitrary complexity. In Proceedings of the 13th International Conference on Pattern Recognition (pp. 880-885). Vienna, Austria.

• Joachims, T. (1998). Making large-scale SVM learning practical. In B. Schölkopf, C. J. C. Burges, & A. J. Smola (Eds.). Advances in kernel methods-support vector learning. Cambridge, MA: MIT Press.

  Google Scholar 

• Joachims, T. (2000). Private communication.

• Laskov, P. (2002). Feasible direction decomposition algorithms for training support vector machines, Machine Learning, 46, 315-349.

  Google Scholar 

• Lin, C.-J. (2000). On the convergence of the decomposition method for support vector machines (Technical Report). Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan. To appear in IEEE Trans. Neural Network.

  Google Scholar 

• Lin, C.-J. & Moré, J. J. (1999). Newton's method for large-scale bound constrained problems. SIAM J. Optim., 9, 1100-1127. (Software available at http://www.mcs.anl.gov/more/tron)

  Google Scholar 

• Mangasarian, O. L. & Musicant, D. R. (1999). Successive overrelaxation for support vector machines. IEEE Trans. Neural Networks, 10:5, 1032-1037.

  Google Scholar 

• Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (1994). Machine learning, neural and statistical classification.

• Englewood Cliffs, N.J.: Prentice Hall. (Data available at anonymous ftp: ftp.ncc.up.pt/pub/statlog/)

• Osuna, E., Freund, R., & Girosi, F. (1997). Training support vector machines: An application to face detection. In Proceedings of CVPR'97.

• Platt, J. C. (1998). Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. J. C. Burges, & A. J. Smola (Eds.). Advances in kernel methods-support vector learning. Cambridge, MA: MIT Press.

  Google Scholar 

• Powell, M. J. D. (1973). On search directions for minimization. Math. Programming, 4, 193-201.

  Google Scholar 

• Saunders, C., Stitson, M. O., Weston, J., Bottou, L., Schölkopf, B., & Smola, A. (1998). Support vector machine reference manual (Technical Report No. CSD-TR-98-03). Egham, UK: Royal Holloway, University of London.

  Google Scholar 

• Schölkopf, B., Burges, C. J. C., & Smola, A. J. (Eds.). (1998). Advances in kernel methods-support vector learning. Cambridge, MA: MIT Press.

  Google Scholar 

• Vanderbei, R. (1994). LOQO: An interior point code for quadratic programming (Technical Report No. SOR 94-15). Statistics and Operations Research, Princeton University. (revised November, 1998)

• Vapnik, V. (1995). The nature of statistical learning theory. New York, NY: Springer-Verlag.

  Google Scholar 

• Vapnik, V. (1998). Statistical learning theory. New York, NY: John Wiley.

  Google Scholar 

Download references