Robust training on approximated minimal-entropy set

no code implementations • 21 Oct 2016 • Tianpei Xie, Nasser. M. Narabadi, Alfred O. Hero

In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set.

Anomaly Detection General Classification +1