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The Savage loss<ref name=":0" /> can be generated using (2) and Table-I as follows
:<math>\phi(v)=C[f^{-1}(v)]+(1-f^{-1}(v))C'[f^{-1}(v)] = (\frac{e^v}{1+e^v})(1-\frac{e^v}{1+e^v})+(1-\frac{e^v}{1+e^v})(1-\frac{2e^v}{1+e^v}) = \frac{1}{(1+e^v)^2}.</math>
The Savage loss is quasi-convex and is bounded for large negative values which makes it less sensitive to outliers. The Savage loss
== Tangent loss ==
The Tangent loss<ref>{{Cite journal|last=Masnadi-Shirazi|first=H.|last2=Mahadevan|first2=V.|last3=Vasconcelos|first3=N.|date=2010-6|title=On the design of robust classifiers for computer vision|url=https://ieeexplore.ieee.org/document/5540136|journal=2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition|pages=779–786|doi=10.1109/CVPR.2010.5540136}}</ref> can be generated using (2) and Table-I as follows
:<math>\phi(v)=C[f^{-1}(v)]+(1-f^{-1}(v))C'[f^{-1}(v)] = 4(\arctan(v)+\frac{1}{2})(1-(\arctan(v)+\frac{1}{2}))+(1-(\arctan(v)+\frac{1}{2}))(4-8(\arctan(v)+\frac{1}{2})) = (2\arctan(v)-1)^2.</math>
The Tangent loss is quasi-convex and is bounded for large negative values which makes it less sensitive to outliers. Interestingly, the Tangent loss also assigns a bounded penalty to data points that have been classified "too correctly". This can help prevent over training on the data set. The Tangent loss has been used in [[Gradient boosting|Gradient Boosting]], the TangentBoost algorithm and Alternating Decision Forests<ref>{{Cite journal|last=Schulter|first=S.|last2=Wohlhart|first2=P.|last3=Leistner|first3=C.|last4=Saffari|first4=A.|last5=Roth|first5=P. M.|last6=Bischof|first6=H.|date=2013-6|title=Alternating Decision Forests|url=https://ieeexplore.ieee.org/document/6618916|journal=2013 IEEE Conference on Computer Vision and Pattern Recognition|pages=508–515|doi=10.1109/CVPR.2013.72}}</ref>.
== Hinge loss ==
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