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• W2137184539 abstract "Multi-view learning has become a hot topic during the past few years. In this paper, we first characterize the sample complexity of multi-view active learning. Under the α-expansion assumption, we get an exponential improvement in the sample complexity from usual Õ(1/ε) to Õ(log 1/ε), requiring neither strong assumption on data distribution such as the data is distributed uniformly over the unit sphere in Rd nor strong assumption on hypothesis class such as linear separators through the origin. We also give an upper bound of the error rate when the α-expansion assumption does not hold. Then, we analyze the combination of multi-view active learning and semi-supervised learning and get a further improvement in the sample complexity. Finally, we study the empirical behavior of the two paradigms, which verifies that the combination of multi-view active learning and semi-supervised learning is efficient." @default.
• W2137184539 created "2016-06-24" @default.
• W2137184539 creator A5010751143 @default.
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• W2137184539 date "2008-01-01" @default.
• W2137184539 modified "2023-09-25" @default.
• W2137184539 title "On multi-view active learning and the combination with semi-supervised learning" @default.
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• W2137184539 doi "https://doi.org/10.1145/1390156.1390301" @default.
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