摘要
半监督学习,与传统的监督学习不同,能同时在少量的已标记数据和大量的未标记数据上进行学习,从而提高性能。协同训练是一种流行的半监督学习算法,已成为目前机器学习和模式识别领域中的一个研究热点。综述半监督学习协同训练的基本思想、研究现状、常用算法,分析目前存在的主要困难,并指出需进一步研究的几个问题。
Different from traditional supervised-learning, semi-supervised can use unlabeled data together with labeled data to improve the performance. Co-training is a popular semi-supervised learnin algorithm and has become a hot topic in the research field of machine learning and pattern recognition. Presents a survey of the main problems and the state-of-art Co-training algorithms. Analyses the main difficulties and points out the questions which need to be solved in the future.
出处
《现代计算机》
2012年第20期8-11,16,共5页
Modern Computer
关键词
机器学习
数据挖掘
半监督学习
协同训练
分类
Machine Learning
Data Mining
Semi-Sensitive Learning
Co-Training
Classification
