题目：Learning with Blind Mind and Random Parameters
报告人：王殿辉（(Justin Wang）教授
澳大利亚La Trobe大学
时间：2017年7月17日上午10:00
地点：计算机学院101会议室
报告摘要
Randomized methods for development of neural networks have great potential to cope with big data processing. This methodology offers a trade-off solution between effectiveness and efficiency. Over the past decades, it has been a common practice to randomly assign the weights and biases of a neural network without any constraint, which results in poor modelling performance due to the existence of junk nodes. This talk reports our findings on the constraint condition and visually demonstrates the significance of our proposed supervisory mechanism to the performance improvement. An original, innovative and effective randomized learning algorithm and resulting randomized learner model, termed as deep stochastic configuration networks (DeepSCNs), are briefly introduced in this talk.
王殿辉（(Justin Wang）教授简历
王殿辉教授1995年3月获东北大学工业自动化专业博士学位，1995-1997在新加坡南洋理工大学电子工程学院做博士后研究工作，1998-2001在香港理工大学计算学系研究员，从事机器学习，数据挖掘和图像处理方面的研究工作。2001年7月至今在澳大利亚La Trobe大学计算机科学与信息技术系从事教学与科研工作。主要研究方向：计算智能与数据挖掘技术在大数据信息处理和智能系统方面的应用研究， 发表研究论文200余篇。目前是IEEE高级会员，博士生导师，任《International Journal of Machine Intelligence and Sensory Signal Processing》主编，《IEEE Transactions on Neural Networks and Learning Systems》、《IEEE Transactions on Cyebernetics》、《Information Sciences》、《 Neurocomputing》等多个国际期刊的副主编。