Discriminative Densities from Maximum Contrast Estimation

Meinicke P, Twellmann T, Ritter H (2003)
In: Advances in Neural Information Processing Systems 15. Proceedings of the 2002 conference. Becker S, Thrun S, Obermayer K (Eds); Cambridge, Mass.: MIT-Press: 1009-1018.

Download
No fulltext has been uploaded. References only!
Conference Paper | Published | English

No fulltext has been uploaded

Author
Editor
Becker, Suzanna ; Thrun, Sebastian ; Obermayer, Klaus
Abstract
We propose a framework for classifier design based on discriminative densities for representation of the differences of the class-conditional distributions in a way that is optimal for classification. The densities are selected from a parametrized set by constrained maximization of some objective function which measures the average (bounded) difference, i.e. the contrast between discriminative densities. We show that maximiza- tion of the contrast is equivalent to minimization of an approximation of the Bayes risk. Therefore using suitable classes of probability density functions, the resulting maximum contrast classifiers(MCCs) can approximate the Bayes rule for the general multiclass case. In particular for a certain parametrization of the density functions we obtain MCCs which have the same functional form as the well-known Support Vector Machines (SVMs). We show that MCC-training in general requires some nonlinear optimization but under certain conditions the problem is concave and can be tackled by a single linear program. We indicate the close relation between SVM- and MCC-training and in particular we show that Linear Programming Machines can be viewed as an approxi- mate realization of MCCs. In the experiments on benchmark data sets, the MCC shows a competitive classification performance.
Publishing Year
Conference
16th Annual Neural Information Processing Systems Conference (NIPS)
Location
British Columbia, Canada
Conference Date
2002-12-09 – 2002-12-14
PUB-ID

Cite this

Meinicke P, Twellmann T, Ritter H. Discriminative Densities from Maximum Contrast Estimation. In: Becker S, Thrun S, Obermayer K, eds. Advances in Neural Information Processing Systems 15. Proceedings of the 2002 conference. Cambridge, Mass.: MIT-Press; 2003: 1009-1018.
Meinicke, P., Twellmann, T., & Ritter, H. (2003). Discriminative Densities from Maximum Contrast Estimation. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in Neural Information Processing Systems 15. Proceedings of the 2002 conference (pp. 1009-1018). Cambridge, Mass.: MIT-Press.
Meinicke, P., Twellmann, T., and Ritter, H. (2003). “Discriminative Densities from Maximum Contrast Estimation” in Advances in Neural Information Processing Systems 15. Proceedings of the 2002 conference, Becker, S., Thrun, S., and Obermayer, K. eds. (Cambridge, Mass.: MIT-Press), 1009-1018.
Meinicke, P., Twellmann, T., & Ritter, H., 2003. Discriminative Densities from Maximum Contrast Estimation. In S. Becker, S. Thrun, & K. Obermayer, eds. Advances in Neural Information Processing Systems 15. Proceedings of the 2002 conference. Cambridge, Mass.: MIT-Press, pp. 1009-1018.
P. Meinicke, T. Twellmann, and H. Ritter, “Discriminative Densities from Maximum Contrast Estimation”, Advances in Neural Information Processing Systems 15. Proceedings of the 2002 conference, S. Becker, S. Thrun, and K. Obermayer, eds., Cambridge, Mass.: MIT-Press, 2003, pp.1009-1018.
Meinicke, P., Twellmann, T., Ritter, H.: Discriminative Densities from Maximum Contrast Estimation. In: Becker, S., Thrun, S., and Obermayer, K. (eds.) Advances in Neural Information Processing Systems 15. Proceedings of the 2002 conference. p. 1009-1018. MIT-Press, Cambridge, Mass. (2003).
Meinicke, Peter, Twellmann, Thorsten, and Ritter, Helge. “Discriminative Densities from Maximum Contrast Estimation”. Advances in Neural Information Processing Systems 15. Proceedings of the 2002 conference. Ed. Suzanna Becker, Sebastian Thrun, and Klaus Obermayer. Cambridge, Mass.: MIT-Press, 2003. 1009-1018.
This data publication is cited in the following publications:
This publication cites the following data publications:

Export

0 Marked Publications

Open Data PUB

Search this title in

Google Scholar
ISBN Search