Rounding Methods for Discrete Linear Classification

@inproceedings{Chevaleyre2013RoundingMF,
  title={Rounding Methods for Discrete Linear Classification},
  author={Yann Chevaleyre and Fr{\'e}d{\'e}ric Koriche and Jean-Daniel Zucker},
  booktitle={ICML},
  year={2013}
}
Learning discrete linear classifiers is known as a difficult challenge. In this paper, this learning task is cast as combinatorial optimization problem: given a training sample formed by positive and negative feature vectors in the Euclidean space, the goal is to find a discrete linear function that minimizes the cumulative hinge loss of the sample. Since this problem is NP-hard, we examine two simple rounding algorithms that discretize the fractional solution of the problem. Generalization… CONTINUE READING
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