Faster Rates in Regression via Active Learning
Faster Rates in Regression via Active Learning
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发表时间：
2005-12
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通讯作者：
R. Castro;R. Willett;R. Nowak
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作者：
R. Castro;R. Willett;R. Nowak
本文提出了严格的统计分析，描述了主动学习显着优于经典被动学习的机制。主动学习算法能够根据先前查询的结果以在线方式进行查询或选择样本位置。在某些情况下，这种额外的灵活性会导致错误衰减率比经典被动学习环境中的错误衰减率显着加快。通过研究两个说明性非参数函数类中主动和被动学习的基本性能限制，探索了这些机制的本质。除了研究主动学习的理论潜力之外，本文还描述了一种实用算法，能够利用主动设置的额外灵活性，并可证明对经典被动技术的改进。我们的主动学习理论和方法在许多应用中显示出前景，包括使用无线传感器网络进行现场估计和断层线检测。
This paper presents a rigorous statistical analysis characterizing regimes in which active learning significantly outperforms classical passive learning. Active learning algorithms are able to make queries or select sample locations in an online fashion, depending on the results of the previous queries. In some regimes, this extra flexibility leads to significantly faster rates of error decay than those possible in classical passive learning settings. The nature of these regimes is explored by studying fundamental performance limits of active and passive learning in two illustrative nonparametric function classes. In addition to examining the theoretical potential of active learning, this paper describes a practical algorithm capable of exploiting the extra flexibility of the active setting and provably improving upon the classical passive techniques. Our active learning theory and methods show promise in a number of applications, including field estimation using wireless sensor networks and fault line detection.