Loss functions for classification

Known as: Logistic loss 
In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the… (More)
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2018
2018
Temporal point processes are a statistical framework for modelling the times at which events of interest occur. The Hawkes… (More)
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2017
2017
In survival analysis, regression models are used to understand the effects of explanatory variables (e.g., age, sex, weight, etc… (More)
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Highly Cited
2015
Highly Cited
2015
In image classification, visual separability between different object categories is highly uneven, and some categories are more… (More)
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2014
2014
We focus on the privacy-utility trade-off encountered by users who wish to disclose some information to an analyst, that is… (More)
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Highly Cited
2008
Highly Cited
2008
The machine learning problem of classifier design is studied from the perspective of probability elicitation, in statistics. This… (More)
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Highly Cited
2007
Highly Cited
2007
We design an online algorithm for Principal Component Analysis. In each trial the current instance is centered and projected into… (More)
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2006
2006
We provide a PAC-Bayesian bound for the expected loss of convex combinations of classifiers under a wide class of loss functions… (More)
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Highly Cited
2005
Highly Cited
2005
What are the natural loss functions or fitting criteria for binary class probability estimation? This question has a simple… (More)
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Highly Cited
2005
Highly Cited
2005
We consider different types of loss functions for discrete ordinal regression, i.e. fitting labels that may take one of several… (More)
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Highly Cited
2004
Highly Cited
2004
In this letter, we investigate the impact of choosing different loss functions from the viewpoint of statistical learning theory… (More)
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