强大的交互式学习
Robust Interactive Learning
摘要：
在本文中，我们提出并研究了一个标准的主动学习模型，其中一个更一般的类型的查询，类条件查询，是允许的泛化。这种查询在应用中非常有用，但一直缺乏理论上的理解。在这项工作中，我们描述了两个著名的噪声模型下的这种查询的功率。我们给出了几乎紧的上限和下限的查询需要学习的一般不可知设置和有界噪声模型的数量。我们进一步表明，我们的方法可以自适应（未知）噪声率，查询复杂性的损失可以忽略不计。
英文摘要：
In this paper we propose and study a generalization of the standard active-learning model where a more general type of query, class conditional query, is allowed. Such queries have been quite useful in applications, but have been lacking theoretical understanding. In this work, we characterize the power of such queries under two well-known noise models. We give nearly tight upper and lower bounds on the number of queries needed to learn both for the general agnostic setting and for the bounded noise model. We further show that our methods can be made adaptive to the (unknown) noise rate, with only negligible loss in query complexity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
