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- W2166984637 abstract "Co-training is a famous semi-supervised learning paradigm exploiting unlabeled data with two views. Most previous theoretical analyses on co-training are based on the assumption that each of the views is sucient to correctly predict the label. However, this assumption can hardly be met in real applications due to feature corruption or various feature noise. In this paper, we present the theoretical analysis on co-training when neither view is sucient. We dene the diversity between the two views with respect to the condence of prediction and prove that if the two views have large diversity, co-training is able to improve the learning performance by exploiting unlabeled data even with insucient views. We also discuss the relationship between view insuciency and diversity, and give some implications for understanding of the dierence between co-training and co-regularization." @default.
- W2166984637 created "2016-06-24" @default.
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- W2166984637 date "2013-10-21" @default.
- W2166984637 modified "2023-09-25" @default.
- W2166984637 title "Co-Training with Insufficient Views" @default.
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