2016/02/18 by Fuqiang Liu, Liu, Fuqiang, Fukun Bi +5
Computer Science · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning and Data Classification
paper · pdf · doi:10.48550/arxiv.1602.05659
openalex publication_date 2016/02/18 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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% labeled data, only if recalls of the two weak classifiers are all greater than zero and the sum of precisions is greater than one. Based on Boost Picking, we present "Test along with Training (TawT)" to improve the generalization of supervised models. Both Boost Picking and TawT are successfully tested in varied little data sets.