Boost Picking: A Universal Method on Converting Supervised Classification to Semi-supervised Classification

18 Feb 2016 Fuqiang Liu Fukun Bi Yiding Yang Liang Chen

This paper proposes a universal method, Boost Picking, to train supervised classification models mainly by un-labeled data. Boost Picking only adopts two weak classifiers to estimate and correct the error... (read more)

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