Loss functions for classification: Difference between revisions

Revision as of 15:47, 4 December 2014

Revision as of 15:48, 4 December 2014

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'''Loss function surrogates for classification''' are computationally feasible [[loss functions]] representing the price we will pay for inaccuracy in our predictions in classification problems. <ref>{{cite doi|10.1162/089976604773135104}}</ref> Specifically, if <math>g: X \mapsto</math> {-1,1} represents the mapping of a vector <math>\mathbf{x} \in X,</math>

Loss