2010
AISTATS
AISTATS 2010
Convexity of Proper Composite Binary Losses
Abstract
A composite loss assigns a penalty to a real-valued prediction by associating the prediction with a probability via a link function then applying a class probability estimation (CPE) loss. If the risk for a composite loss is always minimised by predicting the value associated with the true class probability the composite loss is proper. We provide a novel, explicit and complete characterisation of the convexity of any proper composite loss in terms of its link and its “weight function” associated with its proper CPE loss.
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Conference Pioneer
— AISTATS 2010
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Trend Setter
— Loss Functions
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Keyword Pioneer
— class probability estimation
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Cross-Pollinator
— Artificial Intelligence, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio
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Hot Topic Early Bird
— binary classification