A General Agnostic Active Learning Algorithm
A General Agnostic Active Learning Algorithm
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发表时间：
2007-12
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通讯作者：
S. Dasgupta;Daniel J. Hsu;C. Monteleoni
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作者：
S. Dasgupta;Daniel J. Hsu;C. Monteleoni
针对任意数据分布下的有界VC维假设类，提出了一种不可知的主动学习算法。之前大多数关于主动学习的工作要么做了很强的分布假设，要么在计算上令人望而却步。我们的算法将Cohn， Atlas和Ladner[1]的简单方案扩展到不可知论设置，使用简化的监督学习，以一种简单而微妙的方式利用泛化界限。我们提供了一个回退保证，通过不可知的PAC样本复杂度来限制算法的标签复杂度。我们的分析为某些假设类和分布提供了渐近标签复杂度的改进。我们还通过实验证明了改进。
We present an agnostic active learning algorithm for any hypothesis class of bounded VC dimension under arbitrary data distributions. Most previous work on active learning either makes strong distributional assumptions, or else is computationally prohibitive. Our algorithm extends the simple scheme of Cohn, Atlas, and Ladner [1] to the agnostic setting, using reductions to supervised learning that harness generalization bounds in a simple but subtle manner. We provide a fall-back guarantee that bounds the algorithm's label complexity by the agnostic PAC sample complexity. Our analysis yields asymptotic label complexity improvements for certain hypothesis classes and distributions. We also demonstrate improvements experimentally.