Loss functions for classification: Difference between revisions

Revision as of 02:33, 7 February 2018

Revision as of 14:17, 19 April 2018

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The cross entropy loss is closely related to the [[Kullback-Leibler divergence]] between the empirical distribution and the predicted distribution. This function is not naturally represented as a product of the true label and the predicted value, but is convex and can be minimized using [[stochastic gradient descent]] methods. The cross entropy loss is ubiquitous in modern [[deep learning|deep neural networks]].

==See also==

*[[Statistical learning theory]]

*[[Loss function]]

*[[Support vector machine]]

== References ==