报告题目：SCN-based Unified Random Learners for Large-scale Data Analytics
报告人：Dr. Justin Wang ( La Trobe University, Australia)
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.