Learning from imperfect & related labels
Yan Yan · 2012
Supervised Learning means there is a teacher providing labels or target information given data samples, and the goal is to predict the labels of new or unseen instances. In general, these teachers/labelers may make mistakes. In this thesis, we discuss two supervised learning "imperfect label" scenarios in: (1) multi-label classification and (2) multiple-annotator learning.