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The ROC-Boost Design Algorithm for Asymmetric Classification


In many situations (e.g., cascaded classification), it is desirable to design a classifier with precise constraints on its detection rate or on its false positive rate. We introduce ROC Boost, a modification of the Ada Boost design algorithm that produces asymmetric classifiers with guaranteed detection rate and low false positive rates. Tested in a visual text detection task, ROC-Boost was shown to perform competitively against other popular algorithms. © 2011 IEEE.

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