LOCAL DISTANCE METRIC LEARNING FOR EFFICIENT CONFORMAL PREDICTORS

被引:0
|
作者
Pekala, Michael J. [1 ]
Llorens, Ashley J. [1 ]
Wang, I-Jeng [1 ]
机构
[1] Johns Hopkins Univ, Appl Phys Lab, Laurel, MD 20723 USA
来源
2012 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2012年
关键词
conformal prediction; distance metric learning; classification;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Conformal prediction is a relatively recent approach to classification that offers a theoretical framework for generating predictions with precise levels of confidence. For each new object encountered, a conformal predictor outputs a set of class labels that contains the true label with probability at least 1 - epsilon, where epsilon is a user-specified error rate. The ability to predict with confidence can be extremely useful, but in many real-world applications unambiguous predictions consisting of a single class label are preferred. Hence it is desirable to design conformal predictors to maximize the rate of singleton predictions, termed the efficiency of the predictor. In this paper we derive a novel criterion for maximizing efficiency for a certain class of conformal predictors, show how concepts from local distance metric learning can provide a useful bound for maximizing this criterion, and demonstrate efficiency gains on real-world datasets.
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页数:6
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