Human Semi-Supervised Learning
Human Semi-Supervised Learning
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DOI：
10.1111/tops.12010
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发表时间：
2013-01-01
影响因子：
3
通讯作者：
Zhu, Xiaojin
中科院分区：
文献类型：
--
作者：
Gibson, Bryan R.;Rogers, Timothy T.;Zhu, Xiaojin
大多数人类分类的经验工作都研究了完全监督或完全无监督场景下的学习。然而，大多数真实世界的学习场景都是半监督的：学习者从世界中接收大量未标记的信息，再加上偶尔的经验，其中项目由知识渊博的来源直接标记。机器学习中的大量工作已经研究了学习如何利用提供给学习者的标记和未标记数据。使用人类分类和机器学习研究中发现的模型之间的等效性，我们解释了这些半监督技术如何应用于人类学习。一系列的实验表明，半监督学习模型被证明是有用的解释人类行为时，暴露于标记和未标记的数据。然后，我们讨论了一些机器学习模型，这些模型没有熟悉的人类分类对应物。最后，我们讨论了一些挑战尚未解决的使用半监督模型建模人类分类。
Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization.