Query by transduction
Query by transduction
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DOI：
10.1109/tpami.2007.70811
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发表时间：
2008-09-01
影响因子：
23.6
通讯作者：
Wechsler, Harry
中科院分区：
文献类型：
--
作者：
Ho, Shen-Shyang;Wechsler, Harry
最近人们对使用转导推理进行学习越来越感兴趣。我们在这里将转导推理的范围扩展到基于流的环境中的主动学习。为此，本文提出了查询转导（QBT）作为一种新颖的主动学习算法。 QBT 根据使用转导获得的 p 值查询示例的标签。我们利用转导、贝叶斯统计检验、Kullback-Leibler 散度和香农信息之间的关系表明 QBT 与委员会查询 (QBC) 密切相关。使用支持向量机 (SVM) 作为选择分类器，QBT 的可行性和实用性在二元分类和多类分类任务中得到了体现。我们的实验结果表明，在均值泛化方面，QBT 优于随机抽样、基于委员会的主动学习、基于边际的主动学习和基于流的设置中的 QBC。
There has recently been a growing interest in the use of transductive inference for learning. We expand here the scope of transductive inference to active learning in a stream-based setting. Toward that end, this paper proposes Query-by-Transduction (QBT) as a novel active learning algorithm. QBT queries the label of an example based on the p-values obtained using transduction. We show that QBT is closely related to Query-by-Committee (QBC) using relations between transduction, Bayesian statistical testing, Kullback-Leibler divergence, and Shannon information. The feasibility and utility of QBT is shown on both binary and multiclass classification tasks using a support vector machine (SVM) as the choice classifier. Our experimental results show that QBT compares favorably, in terms of mean generalization, against random sampling, committee-based active learning, margin-based active learning, and QBC in the stream-based setting.