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.
Article PDF
Similar content being viewed by others
References
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.
Fletcher, R. (1987). Practical methods of optimization. New York: Wiley.
Goldberg, D. (1991). What every computer scientist should know about floating-point arithmetic. ACM Computing Surveys, 23(1), 5–48.
Moré, J. J. (1978). The Levenberg–Marquardt algorithm: implementation and theory. In G. Watson (Ed.), Numerical analysis (pp. 105–116). Berlin: Springer.
Nash, S. G., & Sofer, A. (1996). Linear and nonlinear programming. New York: McGraw–Hill.
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.
Nocedal, J., & Wright, S. J. (1999). Numerical optimization. New York: Springer.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Editor: Dale Schuurmans.
Rights and permissions
About this article
Cite this article
Lin, HT., Lin, CJ. & Weng, R.C. A note on Platt’s probabilistic outputs for support vector machines. Mach Learn 68, 267–276 (2007). https://doi.org/10.1007/s10994-007-5018-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10994-007-5018-6