6 citations · 12 across the 3 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2020★ 2 cited
Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)
Ziang Liu, Xue Jiang, Hanbin Luo +3
Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples. Most…
cs.LG2020★ 4 cited
Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression
Ziang Liu, Dongrui Wu
In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approac…
cs.LG2020★ 6 cited
Unsupervised Pool-Based Active Learning for Linear Regression
Ziang Liu, Dongrui Wu
In many real-world machine learning applications, unlabeled data can be easily obtained, but it is very time-consuming and/or expensive to label them. So, it is desirable to be abl…