34 citations · 40 across the 4 of their papers we have counts for
4 papers
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…
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…
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…