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SCN-based Unified Random Learners for Large-scale Data Analytics
发布时间: 2018-08-05     20:19   【返回上一页】 发布人:Justin Wang


北京师范大学数学科学学院

 

 

应用数学学术报告 

 

 

报告题目:SCN-based Unified Random Learners for Large-scale Data Analytics

 

报告人:Dr. Justin Wang  ( La Trobe University, Australia)

 

时间地点:2018年8月7日上午10:00-11:00,后主楼 1220

 

邀 请 人:于福生

 

摘 要:Randomized methods for building feedforward neural networks have great potential to cope with big data analytics. In 2017, we proposed an innovative solution for fast data modelling with stochastic configuration networks (SCNs), which overcome and correct a common pitfall reported in literature over the past decades. This talk reports our recent progresses on SCN-based techniques for large-scale data analytics. An original, innovative and effective randomized learning algorithm, resulting in a unified random learner (URL) model, are introduced with experimental results.