论文标题
布尔功能的采样和学习
Sampling and Learning for Boolean Function
论文作者
论文摘要
在本文中，我们通过引入新工具继续对通用学习机器进行研究。我们首先讨论布尔功能和布尔电路，并建立一组工具，即拟合极值和正确的采样集。我们证明了适当的采样集与布尔电路的复杂性之间的基本关系。然后，我们凭借这套工具，引入了更有效的学习策略。我们表明，通过这样的学习策略和学习动态，可以实现普遍学习，并且需要更少的数据。
In this article, we continue our study on universal learning machine by introducing new tools. We first discuss boolean function and boolean circuit, and we establish one set of tools, namely, fitting extremum and proper sampling set. We proved the fundamental relationship between proper sampling set and complexity of boolean circuit. Armed with this set of tools, we then introduce much more effective learning strategies. We show that with such learning strategies and learning dynamics, universal learning can be achieved, and requires much less data.
