Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers

The predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction accuracy or the so-called Area Under the Curve (AUC). Minimizing the reciprocals of these measures are the goals of supervised learning. However, when the models are constructe...

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Veröffentlicht in:	arXiv.org 2019-02
Hauptverfasser:	Ghanbari, Hiva, Li, Minhan, Scheinberg, Katya
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Approximation Classifiers Machine learning Mathematical analysis Mathematical models Normal distribution Optimization Predictions
Online-Zugang:	Volltext
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