Linear Maximum Margin Classifier for Learning from Uncertain Data

@article{Tzelepis2017LinearMM,
  title={Linear Maximum Margin Classifier for Learning from Uncertain Data},
  author={Christos Tzelepis and Vasileios Mezaris and Ioannis Patras},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2017}
}
In this paper, we propose a maximum margin classifier that deals with uncertainty in data input. More specifically, we reformulate the SVM framework such that each training example can be modeled by a multi-dimensional Gaussian distribution described by its mean vector and its covariance matrix -- the latter modeling the uncertainty. We address the classification problem and define a cost function that is the expected value of the classical SVM cost when data samples are drawn from the multi… CONTINUE READING