Top Cogn Sci. 2013 Jan;5(1):3-12. doi: 10.1111/tops.12004.
This article reviews a number of different areas in the foundations of formal learning theory. After outlining the general framework for formal models of learning, the Bayesian approach to learning is summarized. This leads to a discussion of Solomonoff's Universal Prior Distribution for Bayesian learning. Gold's model of identification in the limit is also outlined. We next discuss a number of aspects of learning theory raised in contributed papers, related to both computational and representational complexity. The article concludes with a description of how semi-supervised learning can be applied to the study of cognitive learning models. Throughout this overview, the specific points raised by our contributing authors are connected to the models and methods under review.
本文回顾了形式学习理论基础的多个不同领域。在概述学习的正式模型的一般框架之后，总结了贝叶斯学习方法。这导致了对 Solomonoff 的贝叶斯学习通用先验分布的讨论。还概述了 Gold 的极限识别模型。接下来，我们讨论了在投稿论文中提出的与计算和表示复杂性有关的学习理论的几个方面。本文最后描述了如何将半监督学习应用于认知学习模型的研究。在整个概述中，我们的投稿作者提出的具体观点与所审查的模型和方法相关联。
