… In this section, we compare our active learning multiple labeler algorithm, active learning+multi…
2009) with multiple annotations and on three UCI Machine Learning Repository (Newman …

… In this paper, a variant of active learning from crowds, namely active learning from crowds
with unsure option, is put forward and investigated. An algorithm called ALCU-SVM is …

… the active learning process. In this paper, we propose a Self-Taught Active Learning (STAL)
paradigm, where a crowd of … labelers are able to form a self-taught learning system and learn …

… machine learning classifiers. By using active learning as our optimization strategy for labeling
tasks in crowdsourced databases, we can minimize the number of questions asked to the …

… of a model learned from the integrated dataset, this paper proposes a framework that
integrates active learning with the self-healing of a model together. With active learning, a limited …

… machine learning classifiers. By using active learning as our optimization strategy for labeling
tasks in crowd-sourced databases, we can minimize the number of questions asked to the …

… Exploiting the wisdom of crowds for multi-label data has … problem of exploiting the wisdom
of crowds for multi-label tasks. … the active learning perspective, where instances are actively …

… 1) Active intelligent recommendation: dynamically sug… back-end active learned model; 2)
Automatic crowd consolidation: … by active learning and auto-consolidation of crowd annotations …

… step in the learning process. In particular, we extend prior work to develop a decision
theoretic active learning framework that jointly considers querying the crowd and domain experts …

… (2009) address the issue of active learning in this scenario—How to jointly learn the
accuracy of labeling sources and obtain the most informative labels for the active learning task? …