论文标题
有限数据的机器学习理论
Theory of Machine Learning with Limited Data
论文作者
论文摘要
机器学习的应用可以理解为通过解释累积的观测值，培训集来获取新知识供实际使用。皮尔斯（Peirce）使用绑架一词进行这种推论。在这里，我将绑架的概念正式化，以实现真正有价值的假设，并表明14位最受欢迎的教科书ML学习者（我测试过的每个学习者），涵盖了分类，回归和聚类，实现了这种绑架推断概念。该方法被提出为统计学习理论的替代方法，该理论要求无限期地假设其为其合理性增加训练设置。
Application of machine learning may be understood as deriving new knowledge for practical use through explaining accumulated observations, training set. Peirce used the term abduction for this kind of inference. Here I formalize the concept of abduction for real valued hypotheses, and show that 14 of the most popular textbook ML learners (every learner I tested), covering classification, regression and clustering, implement this concept of abduction inference. The approach is proposed as an alternative to statistical learning theory, which requires an impractical assumption of indefinitely increasing training set for its justification.
