Minimax Probability Machine

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.

Authors

• Gert Lanckriet
• Laurent Ghaoui
• Chiranjib Bhattacharyya
• Michael Jordan

Keywords

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