Publications (4)
Dual Teaching: A Practical Semi-supervised Wrapper Method
Fuqaing Liu, Chenwei Deng, Fukun Bi +1
Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is…
Feature-Area Optimization: A Novel SAR Image Registration Method
Fuqiang Liu, Fukun Bi, Liang Chen +2
This letter proposes a synthetic aperture radar (SAR) image registration method named Feature-Area Optimization (FAO). First, the traditional area-based optimization model is recon…
Boost Picking: A Universal Method on Converting Supervised Classification to Semi-supervised Classification
Fuqiang Liu, Fukun Bi, Yiding Yang +1
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 estima…
Object Recognition Based on Amounts of Unlabeled Data
Fuqiang Liu, Fukun Bi, Liang Chen
This paper proposes a novel semi-supervised method on object recognition. First, based on Boost Picking, a universal algorithm, Boost Picking Teaching (BPT), is proposed to train a…