机构地区: 华南理工大学计算机科学与工程学院
出 处: 《电子学报》 2004年第12期2051-2055,共5页
摘 要: 增量学习是一种在巩固原有学习成果的基础上快速有效地获取新知识的学习模式 .本文在简述增量学习的相关研究以及排序学习前向掩蔽模型 (SLAM)的特点后 ,提出了一种基于SLAM的快速增量学习算法 .该算法在原神经网络模型分类能力的基础上 ,实现对新增样本的快速增量学习 ,从而在较短的时间内提高该网络模型的分类推广能力 .最后 ,与SLAM算法和Levenberg Marquardt后向传播 (LMBP) Incremental learning mode is meaningful to efficiently acquire additional knowledge on the basis of original knowledge structure. In this paper, the research background of incremental learning and the characteristics of sequential learning ahead masking model (SLAM) are firstly described. Then the fast incremental learning algorithm based on sequential learning ahead masking model is presented. Finally, compared with the SLAM algorithm and Levenberg-Marquardt Back Propagation algorithm, the proposed algorithm is testified to have the better computation efficiency and generalization ability.
关 键 词: 排序学习前向掩蔽模型 增量学习 神经网络
领 域: [自动化与计算机技术] [自动化与计算机技术]