Information Planning for Text Data
Massachusetts Institute of Technology（麻省理工学院）
英文摘要：
Information planning enables faster learning with fewer training examples. It is particularly applicable when training examples are costly to obtain. This work examines the advantages of information planning for text data by focusing on three supervised models: Naive Bayes, supervised LDA and deep neural networks. We show that planning based on entropy and mutual information outperforms random selection baseline and therefore accelerates learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
