A Computational Model of Self-Supervised Learning Based-on Adaptive Resonance Theory

摘要：

　　Computational models of learning are typically divided into supervised learning, unsupervised learning,Computational models of learning are typically divided into supervised learning, unsupervised learning,for expanding incomplete knowledge, via self-directed learning that incorporates knowledge not previously experienced. This article defines a new self-supervised learning framework to address these pregnant learning contexts, and implements this framework using adaptive resonance theory. The learning framework learns about novel features from unlabeled patterns without destroying knowledge previously acquired from labeled patterns.

关键词： 自我监督学习 计算模型 学习框架

作者: XIE Wen-biao FAN Shao-sheng CHEN Zhong FEI Hong-xiao

作者单位: School of Electrical & Information Engineering, Changsha University of Science and Technology, Chang School of Electrical & Information Engineering, Changsha University of Science and Technology, Chang School of Information Science and Engineering, Central South University, Changsha 410083, China

会议类型: 国内会议

会议名称: 第二届全国语言动力系统研讨会

会议地点: 长沙

会议语种:英文

页码: 1-8

在线出版日期: 2011-11-02（万方平台首次上网日期，不代表论文的发表时间）