Dept. of Comput. Sci., City Univ. of Hong Kong, Hong Kong, China;
learning (artificial intelligence); pattern classification; unsolicited e-mail; classification model; email classification system; labeled data; multiview data; multiview disagreement-based semisupervised learning; single view data; spam; unlabeled data; Data models; Electronic mail; Feature extraction; Semisupervised learning; Supervised learning; Support vector machines; Training; Email Classification; Machine Learning Applications; Multi-View; Network Security; Semi-Supervised Learning;
机译:通过半监督学习为物联网系统基于多视图的电子邮件分类设计
机译:多样性促进半监督分类的多视图图学习
机译:SAR目标识别半监督学习多视图分类
机译:使用数据减少和基于分歧的半监督学习增强电子邮件分类
机译:基于监督的入侵检测系统的位掩码对的半监督学习。
机译:基于半监督的基于学习的学习诊断分类方法使用人工神经网络
机译:使用基于多视图的半监督学习设计电子邮件分类系统