论文标题
通过迭代学习者学习半空间和其他概念课程
Learning Half-Spaces and other Concept Classes in the Limit with Iterative Learners
论文作者
论文摘要
为了建模有效的学习范式，迭代学习算法一一访问数据，更新当前的假设而无需回归到过去的数据。例如，对迭代学习的过去研究进行了分析，例如，许多重要的其他要求及其对迭代学习者的影响。在本文中，我们的结果是双重的。首先，我们分析了迭代学习的各种环境的相对学习能力，包括从文本和线人学习以及各种进一步的限制，例如，我们表明，强烈的非U形学习对从线人的迭代学习是限制的。其次，我们研究了半空间概念类别的可学习性，并提供了一种建设性的迭代算法，以从线人那里学习一组半空间。
In order to model an efficient learning paradigm, iterative learning algorithms access data one by one, updating the current hypothesis without regress to past data. Past research on iterative learning analyzed for example many important additional requirements and their impact on iterative learners. In this paper, our results are twofold. First, we analyze the relative learning power of various settings of iterative learning, including learning from text and from informant, as well as various further restrictions, for example we show that strongly non-U-shaped learning is restrictive for iterative learning from informant. Second, we investigate the learnability of the concept class of half-spaces and provide a constructive iterative algorithm to learn the set of half-spaces from informant.
