2009 · 43 citations · 15 references
Natural SciencesMathematicsDecision TheoryMachine LearningOptimizationConvex AnalysisConvex OptimizationLoss FunctionStochastic OptimizationBregman DivergenceLink FunctionsMargin-based LossSurrogate Loss
We present tight surrogate regret bounds for the class of proper (i.e., Fisher consistent) losses. The bounds generalise the margin-based bounds due to Bartlett et al. (2006). The proof uses Taylor's theorem and leads to new representations for loss and regret and a simple proof of the integral representation of proper losses. We also present a different formulation of a duality result of Bregman divergences which leads to a simple demonstration of the convexity of composite losses using canonical link functions.
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Clustering with Bregman Divergences
Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon et al. · 2004 · 1.5K citations