论文标题
测量理论方法的非均匀可学习性
Measure Theoretic Approach to Nonuniform Learnability
论文作者
论文摘要
早期引入的非均匀可学习性表征，可以使用量度理论方法重新定义样本量取决于比较学习者的假设。在非均匀的可学习性中，严格放松了可能的近似正确的框架。引入了一种新算法，概括度量的可学习性框架，以研究其样本和计算复杂性界限。像最小描述长度原则一样，这种方法可以被视为Occam Razor的解释。此外，提出了许多情况，可以使用GML框架的假设类别，我们可以学会使用GML方案并实现统计一致性。
An earlier introduced characterization of nonuniform learnability that allows the sample size to depend on the hypothesis to which the learner is compared has been redefined using the measure theoretic approach. Where nonuniform learnability is a strict relaxation of the Probably Approximately Correct framework. Introduction of a new algorithm, Generalize Measure Learnability framework, to implement this approach with the study of its sample and computational complexity bounds. Like the Minimum Description Length principle, this approach can be regarded as an explication of Occam razor. Furthermore, many situations were presented, Hypothesis Classes that are countable where we can apply the GML framework, which we can learn to use the GML scheme and can achieve statistical consistency.
