学习曲线的形状：一个评论
The Shape of Learning Curves: a Review
摘要：
学习曲线提供了对学习者的泛化性能对训练集大小的依赖性的洞察。这一重要工具可用于模型选择，预测更多训练数据的效果，并降低模型训练和超参数调整的计算复杂性。本文回顾了这个术语的起源，提供了学习曲线的正式定义，并简要介绍了其估计等基础知识。我们的主要贡献是一个全面的文献综述学习曲线的形状。我们讨论的经验和理论证据，支持良好的曲线，往往有一个幂律或指数的形状。我们考虑高斯过程的学习曲线，它们可以显示的复杂形状，以及影响它们的因素。我们特别注意那些表现不佳的学习曲线的例子，这些例子显示出更多训练数据的学习性能更差。最后，我们指出了各种开放的问题，值得更深入的实证和理论研究。总而言之，我们的审查强调，学习曲线是惊人的多样化，没有通用的模型可以确定。
英文摘要：
Learning curves provide insight into the dependence of a learner's generalization performance on the training set size. This important tool can be used for model selection, to predict the effect of more training data, and to reduce the computational complexity of model training and hyperparameter tuning. This review recounts the origins of the term, provides a formal definition of the learning curve, and briefly covers basics such as its estimation. Our main contribution is a comprehensive overview of the literature regarding the shape of learning curves. We discuss empirical and theoretical evidence that supports well-behaved curves that often have the shape of a power law or an exponential. We consider the learning curves of Gaussian processes, the complex shapes they can display, and the factors influencing them. We draw specific attention to examples of learning curves that are ill-behaved, showing worse learning performance with more training data. To wrap up, we point out various open problems that warrant deeper empirical and theoretical investigation. All in all, our review underscores that learning curves are surprisingly diverse and no universal model can be identified.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
