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- W2953223201 abstract "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 $widetilde{O}(logfrac{1}{epsilon})$, 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." @default.
- W2953223201 created "2019-06-27" @default.
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- W2953223201 date "2010-05-31" @default.
- W2953223201 modified "2023-09-25" @default.
- W2953223201 title "Multi-View Active Learning in the Non-Realizable Case" @default.
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