Active Learning Based on Information Entropy for Semi-supervised Classification
Jie Shen · Computer Technology and Development · 2010
Most of supervised machine learning methods led to poor performance when work on limited tagged data.Investigated a novel semi-supervised learning method based on active learning with information entropy.An optimization strategy of selecting part of instances from unlabeled examples for classifying in each iteration,based on active learning from unlabeled examples,was presented.The experiment results show that our method achieve high performance on small tagged data.