Robust Logistic Regression using Shift Parameters

  title={Robust Logistic Regression using Shift Parameters},
  author={Julie Tibshirani and Christopher D. Manning},
Annotation errors can significantly hurt classifier performance, yet datasets are only growing noisier with the increased use of Amazon Mechanical Turk and techniques like distant supervision that automatically generate labels. In this paper, we present a robust extension of logistic regression that incorporates the possibility of mislabelling directly into the objective. This model can be trained through nearly the same means as logistic regression, and retains its efficiency on… CONTINUE READING