Topics in Computational Learning Theory

这项研究项目致力于各种自然和人工学习系统的计算学习理论、算法和形式化方法的研究。本项目涉及以下具体主题：在不经意的环境下在线学习的复杂性；在线学习的随机性的力量；在线学习的空间效率算法设计；容错在线学习算法的设计与分析；一阶谓词逻辑片断中定义的在线学习；分析常用计算神经网络模型的计算能力；设计和分析深度为2的神经网络概念类的学习算法。

This research project addresses problems in computational learning theory, the investigation of algorithms and formal methods for various natural and artificial learning systems. The following specific topics are addressed in this project: the complexity of on-line learning with an oblivious environment; the power of randomization for on-line learning; design of space-efficient algorithms for on-line learning; design and analysis of error-tolerant on-line learning algorithms; on-line learning of definitions in fragments of first order predicate logic; analysis of the computational power of common computational neural network models; design and analysis of learning algorithms for concept classes on neural networks of depth 2.