不充分视图半监督学习的理论分析研究

项目名称： 不充分视图半监督学习的理论分析研究

项目编号： No.61305067

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 王魏

作者单位： 南京大学

项目金额： 26万元

中文摘要： 在实际应用中，多视图往往伴随大量未标记数据同时出现，然而由于属性的退化和各种噪声的影响，每个视图可能都是不充分的，本项目关注于不充分视图半监督学习的理论研究，针对半监督学习中最重要的风范之一协同训练，给出不充分视图协同训练的理论分析；针对目前最接近真实问题的理论设置Tsybakov 条件，给出Tsybakov 条件下不充分视图协同训练的理论分析；针对协同正则化，完善不充分视图协同正则化的理论分析并研究其与协同训练的本质区别；依据理论分析得到的结果，设计并实现不充分视图半监督学习算法。本项目可望在国际期刊、会议和国内一级学报上发表论文4-6篇，申请专利1-2项，培养研究生2名。

中文关键词： 机器学习；计算学习理论；半监督学习；Tsybakov条件；

英文摘要： In real-world applications, multiple views come along with a large amount of unlabeled data. However, each of the views may not be sufficient due to feature corruption or various noise. In this research project, we focus on the theoretical study on semi-supervised learning with insufficient views. We try to provide the theoretical analysis for co-training with insufficient views, try to provide the theoretical analysis for co-training with insufficient views under Tsybakov condition, try to complement the theoretical analysis for co-regularization and study the difference between co-training and co-regularization, and develop new semi-supervised learning algorithms dealing with insufficient views based on the theoretical results. It is expected to publish 4-6 papers on important international journals and conferences and native top journals, apply 1-2 patents, and supervise 2 graduate students.

英文关键词： machine learning；computational learning theory；semi-supervised learning；Tsybakov condition；