3 papers
cs.LG2019
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion
Bo Du, Zengmao Wang, Lefei Zhang +2
Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also h…
cs.LG2019
Exploring Representativeness and Informativeness for Active Learning
Bo Du, Zengmao Wang, Lefei Zhang +4
How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an…
cs.LG2018
Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach
Xi Fang, Zengmao Wang, Xinyao Tang +1
Labeling each instance in a large dataset is extremely labor- and time- consuming . One way to alleviate this problem is active learning, which aims to which discover the most valu…