Date of this Version
Advances in Neural Information Processing Systems
We develop a theory of online learning by defining several complexity measures. Among them are analogues of Rademacher complexity, covering numbers and fatshattering dimension from statistical learning theory. Relationship among these complexity measures, their connection to online learning, and tools for bounding them are provided. We apply these results to various learning problems. We provide a complete characterization of online learnability in the supervised setting.
Rakhlin, A., Sridharan, K., & Tewari, A. (2010). Online Learning: Random Averages, Combinatorial Parameters, and Learnability. Advances in Neural Information Processing Systems, 23 1984-1992. Retrieved from https://repository.upenn.edu/statistics_papers/128
Date Posted: 27 November 2017