Machine Learning

, Volume 68, Issue 3, pp 267–276 | Cite as

A note on Platt’s probabilistic outputs for support vector machines

TECHNICAL NOTE

Abstract

Platt’s probabilistic outputs for Support Vector Machines (Platt, J. in Smola, A., et al. (eds.) Advances in large margin classifiers. Cambridge, 2000) has been popular for applications that require posterior class probabilities. In this note, we propose an improved algorithm that theoretically converges and avoids numerical difficulties. A simple and ready-to-use pseudo code is included.

Keywords

Support vector machine Posterior probability 

References

  1. Chang, C.-C., & Lin, C.-J. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
  2. Fletcher, R. (1987). Practical methods of optimization. New York: Wiley. MATHGoogle Scholar
  3. Goldberg, D. (1991). What every computer scientist should know about floating-point arithmetic. ACM Computing Surveys, 23(1), 5–48. CrossRefGoogle Scholar
  4. Moré, J. J. (1978). The Levenberg–Marquardt algorithm: implementation and theory. In G. Watson (Ed.), Numerical analysis (pp. 105–116). Berlin: Springer. CrossRefGoogle Scholar
  5. Nash, S. G., & Sofer, A. (1996). Linear and nonlinear programming. New York: McGraw–Hill. Google Scholar
  6. Newman, D. J., Hettich, S., Blake, C. L., & Merz, C. J. (1998). UCI repository of machine learning databases (Technical report). Department of Information and Computer Sciences, University of California, Irvine. Google Scholar
  7. Nocedal, J., & Wright, S. J. (1999). Numerical optimization. New York: Springer. MATHGoogle Scholar
  8. Platt, J. (2000). Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In A. Smola, P. Bartlett, B. Schölkopf, & D. Schuurmans (Eds.), Advances in large margin classifiers. Cambridge: MIT Press. Google Scholar
  9. Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1992). Numerical recipes: the art of scientific computing (2nd ed.). Cambridge: Cambridge University Press. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.Department of StatisticsNational Chengchi UniversityTaipeiTaiwan

Personalised recommendations