no code implementations • 12 Nov 2016 • Fuqaing Liu, Chenwei Deng, Fukun Bi, Yiding Yang
Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data.
no code implementations • 25 Mar 2016 • Fuqiang Liu, Fukun Bi, Liang Chen
Using 2% labeled data and 98% unlabeled data, the accuracies of the proposed method on the two data sets are 78. 39% and 50. 77% respectively.
no code implementations • 18 Feb 2016 • Fuqiang Liu, Fukun Bi, Liang Chen, Hao Shi, Wei Liu
This letter proposes a synthetic aperture radar (SAR) image registration method named Feature-Area Optimization (FAO).
no code implementations • 18 Feb 2016 • Fuqiang Liu, Fukun Bi, Yiding Yang, Liang Chen
It is theoretically proved that Boost Picking could train a supervised model mainly by un-labeled data as effectively as the same model trained by 100% labeled data, only if recalls of the two weak classifiers are all greater than zero and the sum of precisions is greater than one.