How to implement SVMs

IEEE Intelligent Systems and Their Applications

Volumn 13, Issue 4, 1998, Pages 26-28

  Platt, John ^a,b  

^a MICROSOFT RESEARCH   (United States)

^b CALIFORNIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0005644468     PISSN: 10947167     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (8)

1. V. Vapnik, Estimation of Dependencies Based on Empirical Data, Springer-Verlag. New York, 1982.
2. C.J.C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," submitted to Data Mining and Knowledge Discovery, 1998.
3. J.C. Platt, "Fast Training of SVMs Using Sequential Minimal Optimization," to be published in Advances in Kernel Methods - Support Vector Learning, B. Schölkopf, C. Burges, and A. Smola, eds., MIT Press. Cambridge, Mass., 1998.
4. L. Kaufman, "Solving the Quadratic Programming Problem Arising in Support Vector Classification," to be published in Advances in Kernel Methods - Support Vector Learning, MIT Press, 1998.
5. E. Osuna, R. Freund, and F. Girosi, "An Improved Training Algorithm for Support Vector Machines," Proc. IEEE Neural Networks for Signal Processing VII Workshop, IEEE Press, Piscataway, N.J., 1997, pp. 276-285.
6. T. Joachims, "Making Large-Scale SVM Learning Practical," to be published in Advances in Kernel Methods - Support Vector Learning, MIT Press, 1998.
7. J.C. Platt, Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, Microsoft Research Tech. Report MSR-TR-98-14, Microsoft, Redmond, Wash., 1998.
8. C.J. Merz and P.M. Murphy, UCI Repository of Machine Learning Databases, Univ. of California, Irvine, Dept. Information and Computer Science, Irvine, Calif.; http://www. ics.uci.edu/∼mlearn/MLRepository.html.