标签排序与弃权：预测偏序通过保留概率分布（扩展摘要）
Label Ranking with Abstention: Predicting Partial Orders by Thresholding Probability Distributions (Extended Abstract)
摘要：
我们考虑扩展的标签排名的设置，其中学习者被允许作出预测的形式，而不是总订单的部分。这类预测被解释为一种部分的判断：如果学习者对两个选择的相对顺序没有足够的把握，它可能会放弃这一决定，而是宣布这些选择是不可比的。我们提出了一种新的学习方法来预测偏序，改进现有的方法，无论是理论上和经验。我们的方法是基于阈值的标签之间的成对偏好的概率的想法，由一个预测（参数化）的概率分布上的所有排名。
英文摘要：
We consider an extension of the setting of label ranking, in which the learner is allowed to make predictions in the form of partial instead of total orders. Predictions of that kind are interpreted as a partial abstention: If the learner is not sufficiently certain regarding the relative order of two alternatives, it may abstain from this decision and instead declare these alternatives as being incomparable. We propose a new method for learning to predict partial orders that improves on an existing approach, both theoretically and empirically. Our method is based on the idea of thresholding the probabilities of pairwise preferences between labels as induced by a predicted (parameterized) probability distribution on the set of all rankings.
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。
