标题： 一种基于熵的分层学习模型 显示英文标题

标题： An Entropy-Based Model for Hierarchical Learning

摘要： 机器学习是人工智能的主要方法，通过这种方法，计算机从数据和经验中学习。 在监督学习的框架下，计算机要准确高效地从数据中学习，需要通过学习模型提供关于数据分布和目标函数的辅助信息。 这种辅助信息的概念与统计学习理论中的正则化概念相关。 现实世界数据集的一个共同特点是数据领域是多尺度的，目标函数是良好行为且平滑的。 本文提出了一种基于熵的学习模型，利用这种数据结构，并讨论其统计和计算上的优势。 分层学习模型受到人类逻辑和渐进式由易到难学习机制的启发，并具有可解释的层次。 该模型根据数据实例和目标函数的复杂性分配计算资源。 这一特性可以带来多种好处，包括在为许多用户训练模型或训练中断时的更高推理速度和计算节省。 我们使用多尺度熵对学习机制进行了统计分析，并表明它可以比均匀收敛界限产生显著更强的保证。 显示英文摘要

摘要： Machine learning is the dominant approach to artificial intelligence, through which computers learn from data and experience. In the framework of supervised learning, a necessity for a computer to learn from data accurately and efficiently is to be provided with auxiliary information about the data distribution and target function through the learning model. This notion of auxiliary information relates to the concept of regularization in statistical learning theory. A common feature among real-world datasets is that data domains are multiscale and target functions are well-behaved and smooth. This paper proposes an entropy-based learning model that exploits this data structure and discusses its statistical and computational benefits. The hierarchical learning model is inspired by human beings' logical and progressive easy-to-hard learning mechanism and has interpretable levels. The model apportions computational resources according to the complexity of data instances and target functions. This property can have multiple benefits, including higher inference speed and computational savings in training a model for many users or when training is interrupted. We provide a statistical analysis of the learning mechanism using multiscale entropies and show that it can yield significantly stronger guarantees than uniform convergence bounds.