Minimax Probability Machine
Abstract
When constructing a classifier, the probability of correct classifi(cid:173) cation of future data points should be maximized. In the current paper this desideratum is translated in a very direct way into an optimization problem, which is solved using methods from con(cid:173) vex optimization. We also show how to exploit Mercer kernels in this setting to obtain nonlinear decision boundaries. A worst-case bound on the probability of misclassification of future data is ob(cid:173) tained explicitly.
Cite
Text
Lanckriet et al. "Minimax Probability Machine." Neural Information Processing Systems, 2001.Markdown
[Lanckriet et al. "Minimax Probability Machine." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/lanckriet2001neurips-minimax/)BibTeX
@inproceedings{lanckriet2001neurips-minimax,
title = {{Minimax Probability Machine}},
author = {Lanckriet, Gert and Ghaoui, Laurent E. and Bhattacharyya, Chiranjib and Jordan, Michael I.},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {801-807},
url = {https://mlanthology.org/neurips/2001/lanckriet2001neurips-minimax/}
}