Learning Linear Discriminant Analysis in Real World Applications
讲座名称：
Learning Linear Discriminant Analysis in Real World Applications
讲座时间：
2009-07-03
讲座人：
庞韶宁
形式：
校区：
兴庆校区
实践学分：
讲座内容：
Linear Discriminant Analysis (LDA) has been widely researched to implement various computation intelligences, such as pattern recognition, data mining, bioinformatics and robotics. However, learning LDA in real world confronts difficulties in different application scenarios. Starting from classic LDA, this talk introduces a series of recent LDA developments, where an LDA model is enabled to be learned either in one batch session, or incrementally by ILDA through instance-space merging, or through LDA eigenspace merging; In multi-agent background, LDA can be learned cooperatively by a number of agents with knowledge sharing in-between each other; In a special case, a created LDA can be renovated by LDA splitting with a minimum processing on the raw data instance; Even In some physical limited environment such as the remote space, LDA can be actively learned by an independent agent on fewer selected curiosity instances, or by a multiple of agents in a competitive and cooperative learning manner.
相关视频