From Convex to Nonconvex: A Loss Function Analysis for Binary Classification

@article{Zhao2010FromCT,
  title={From Convex to Nonconvex: A Loss Function Analysis for Binary Classification},
  author={Lei Zhao and Musa A. Mammadov and John Yearwood},
  journal={2010 IEEE International Conference on Data Mining Workshops},
  year={2010},
  pages={1281-1288}
}
Problems of data classification can be studied in the framework of regularization theory as ill-posed problems. In this framework, loss functions play an important role in the application of regularization theory to classification. In this paper, we review some important convex loss functions, including hinge loss, square loss, modified square loss, exponential loss, logistic regression loss, as well as some non-convex loss functions, such as sigmoid loss, $\phi$-loss, ramp loss, normalized… CONTINUE READING
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