Rounding Methods for Discrete Linear Classification

  title={Rounding Methods for Discrete Linear Classification},
  author={Yann Chevaleyre and Fr{\'e}d{\'e}ric Koriche and Jean-Daniel Zucker},
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
Highly Cited
This paper has 50 citations. REVIEW CITATIONS


Publications citing this paper.

51 Citations

Citations per Year
Semantic Scholar estimates that this publication has 51 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…