非对称分类的ROC-Boost设计算法

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI:10.1109/ICMLA.2011.142

Guido Cesare, R. Manduchi

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引用次数: 0

摘要

在许多情况下(例如，级联分类)，我们希望设计一个分类器，对其检测率或假阳性率有精确的约束。我们介绍了ROC Boost，它是Ada Boost设计算法的一种改进，可以产生具有保证检测率和低假阳性率的非对称分类器。在视觉文本检测任务中进行测试，ROC-Boost被证明可以与其他流行的算法进行竞争。

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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.

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2011 10th International Conference on Machine Learning and Applications and Workshops

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