Fuqiang Liu

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  • Abedan Kondori, F Liu, Abedan Farid, Shahrouz Kondori, Li Yousefi, Liu
  • 2013
—Movement disorders prevent many people from enjoying their daily lives. As with other diseases, diagnosis and analysis are key issues in treating such disorders. Computer vision-based motion capture systems are helpful tools for accomplishing this task. However Classical motion tracking systems suffer from several limitations. First they are not cost(More)
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. 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%(More)
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