对最佳实例进行排名
Ranking the best instances
摘要：
我们制定的双边排名的目标是专注于最好的情况下，在框架中的本地排名问题。我们提出了一种方法的基础上建设的实值评分功能。我们研究经验风险最小化的专用统计，其中涉及经验分位数的分数。我们首先说明的问题，找到最好的例子，可以作为一个分类问题与质量约束。接下来，我们开发了特殊的性能指标的局部排名问题，扩展了ROC曲线（AUC/AROC）标准下的面积，并描述了这些新的标准的最佳元素。我们还强调了这样一个事实，即对最佳实例进行排名的目标不能以阶段方式实现，首先，最佳实例将被暂时识别，然后可以应用标准AUC标准。最后，我们国家的初步统计结果的本地排名问题。
英文摘要：
We formulate the local ranking problem in the framework of bipartite ranking where the goal is to focus on the best instances. We propose a methodology based on the construction of real-valued scoring functions. We study empirical risk minimization of dedicated statistics which involve empirical quantiles of the scores. We first state the problem of finding the best instances which can be cast as a classification problem with mass constraint. Next, we develop special performance measures for the local ranking problem which extend the Area Under an ROC Curve (AUC/AROC) criterion and describe the optimal elements of these new criteria. We also highlight the fact that the goal of ranking the best instances cannot be achieved in a stage-wise manner where first, the best instances would be tentatively identified and then a standard AUC criterion could be applied. Eventually, we state preliminary statistical results for the local ranking problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
