Multi-View Active Learning in the Non-Realizable Case

The sample complexity of active learning under the realizability assumption has been well-studied. The realizability assumption, however, rarely holds in practice. In this paper, we theoretically characterize the sample complexity of active learning in the non-realizable case under multi-view setting. We prove that, with unbounded Tsybakov noise, the sample complexity of multi-view active learning can be $\widetilde{O}(\log\frac{1}{\epsilon})$, contrasting to single-view setting where the polynomial improvement is the best possible achievement. We also prove that in general multi-view setting the sample complexity of active learning with unbounded Tsybakov noise is $\widetilde{O}(\frac{1}{\epsilon})$, where the order of $1/\epsilon$ is independent of the parameter in Tsybakov noise, contrasting to previous polynomial bounds where the order of $1/\epsilon$ is related to the parameter in Tsybakov noise.Comment: 22 pages, 1 figur

Online-offline activities and game-playing behaviors of avatars in a massive multiplayer online role-playing game

Massive multiplayer online role-playing games (MMORPGs) are very popular in China, which provides a potential platform for scientific research. We study the online-offline activities of avatars in an MMORPG to understand their game-playing behavior. The statistical analysis unveils that the active avatars can be classified into three types. The avatars of the first type are owned by game cheaters who go online and offline in preset time intervals with the online duration distributions dominated by pulses. The second type of avatars is characterized by a Weibull distribution in the online durations, which is confirmed by statistical tests. The distributions of online durations of the remaining individual avatars differ from the above two types and cannot be described by a simple form. These findings have potential applications in the game industry.Comment: 6 EPL pages including 10 eps figure