基于局部随机罚分的复杂性正则化
Complexity regularization via localized random penalties
摘要：
本文研究了非参数分类问题中基于惩罚经验损失最小化的模型选择问题。数据相关的处罚，这是基于每个模型类的一个小子类的复杂性估计，只包含那些功能与小的经验损失。处罚是新颖的，因为那些在文献中考虑的通常是基于整个模型类。Oracle不等式使用这些惩罚建立，并证明了新的惩罚的优势，比那些基于整个模型类的复杂性。
英文摘要：
In this article, model selection via penalized empirical loss minimization in nonparametric classification problems is studied. Data-dependent penalties are constructed, which are based on estimates of the complexity of a small subclass of each model class, containing only those functions with small empirical loss. The penalties are novel since those considered in the literature are typically based on the entire model class. Oracle inequalities using these penalties are established, and the advantage of the new penalties over those based on the complexity of the whole model class is demonstrated.
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。
