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 O(log 1 ), 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 O(1 ), where the order of 1/ is independent of the parameter in Tsybakov noise, contrasting to previous polynomial bounds where the order of 1/ is related to the parameter in Tsybakov noise.

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Wei Wang, Zhi-Hua Zhou
• Keywords: CORR 2010, Education, Realizability Assumption, Sample Complexity, Tsybakov Noise