題目：Learning with Blind Mind and Random Parameters

報(bào)告人：王殿輝（(Justin Wang）教授  

     澳大利亞La Trobe大學(xué)

時(shí)間：2017年7月17日上午10:00

地點(diǎn)：計(jì)算機(jī)學(xué)院101會(huì)議室

報(bào)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.

王殿輝（(Justin Wang）教授簡(jiǎn)歷

王殿輝教授1995年3月獲東北大學(xué)工業(yè)自動(dòng)化專業(yè)博士學(xué)位，1995-1997在新加坡南洋理工大學(xué)電子工程學(xué)院做博士后研究工作，1998-2001在香港理工大學(xué)計(jì)算學(xué)系研究員，從事機(jī)器學(xué)習(xí)，數(shù)據(jù)挖掘和圖像處理方面的研究工作。2001年7月至今在澳大利亞La Trobe大學(xué)計(jì)算機(jī)科學(xué)與信息技術(shù)系從事教學(xué)與科研工作。主要研究方向：計(jì)算智能與數(shù)據(jù)挖掘技術(shù)在大數(shù)據(jù)信息處理和智能系統(tǒng)方面的應(yīng)用研究， 發(fā)表研究論文200余篇。目前是IEEE高級(jí)會(huì)員，博士生導(dǎo)師，任《International Journal of Machine Intelligence and Sensory Signal Processing》主編，《IEEE Transactions on Neural Networks and Learning Systems》、《IEEE Transactions on Cyebernetics》、《Information Sciences》、《 Neurocomputing》等多個(gè)國(guó)際期刊的副主編。