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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Transfer Ordinal Label Learning
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Transfer Ordinal Label Learning

机译：转移顺序标签学习

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摘要

Designing a classifier in the absence of labeled data is becoming a common encounter as the acquisition of informative labels is often difficult or expensive, particularly on new uncharted target domains. The feasibility of attaining a reliable classifier for the task of interest is embarked by some in transfer learning, where label information from relevant source domains is considered for complimenting the design process. The core challenge arising from such endeavors, however, is the induction of source sample selection bias, such that the trained classifier has the tendency of steering toward the distribution of the source domain. In addition, this bias is deemed to become more severe on data involving multiple classes. Considering this cue, our interest in this paper is to address such a challenge in the target domain, where ordinal labeled data are unavailable. In contrast to the previous works, we propose a transfer ordinal label learning paradigm to predict the ordinal labels of target unlabeled data by spanning the feasible solution space with ensemble of ordinal classifiers from the multiple relevant source domains. Specifically, the maximum margin criterion is considered here for the construction of the target classifier from an ensemble of source ordinal classifiers. Theoretical analysis and extensive empirical studies on real-world data sets are presented to study the benefits of the proposed method.
机译：在缺乏标记数据的情况下设计分类器已成为一种常见的挑战，因为获取信息性标记通常很困难或昂贵，尤其是在新的未知领域。在迁移学习中，有人为感兴趣的任务获得可靠的分类器是可行的，在转移学习中，考虑了来自相关源域的标签信息以补充设计过程。然而，由这些努力引起的核心挑战是引起源样本选择偏差，使得训练有素的分类器具有转向源域的分布的趋势。此外，对于涉及多个类别的数据，这种偏见被认为更加严重。考虑到这一提示，我们在本文中的兴趣是解决目标领域中的挑战，因为在这些领域中没有序数标签数据。与以前的工作相反，我们提出了一种转移序数标签学习范式，通过跨越多个相关源域中序数分类器的集合跨越可行解空间来预测目标未标记数据的序数标签。具体而言，在此考虑从源序数分类器的集合构建目标分类器的最大余量标准。提出了对实际数据集的理论分析和广泛的经验研究，以研究该方法的好处。

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