Topics in Computational Learning Theory

这个研究项目解决了计算中的问题。 学习理论，算法和形式化方法的研究 用于各种自然和人工学习系统。 以下 该项目涉及的具体主题是： 在线学习的复杂性， 环境; 在线学习的随机化能力; 设计空间有效的在线学习算法; 容错在线学习的设计与分析 算法; 在线学习一阶片段中的定义 谓词逻辑; 常用计算器的计算能力分析 神经网络模型; 概念学习算法的设计与分析 深度为2的神经网络类。

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.