Robust Logistic Regression using Shift Parameters (Long Version)

21 May 2013Julie TibshiraniChristopher 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... (read more)

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