Extreme learning machine for regression and classification, Dr. Huang Guangbin, 新加坡南洋理工大学电子与电气工程学院

报告人：Dr. Huang Guangbin (新加坡南洋理工大学电子与电气工程学院 副教授)  

报告题目：Extreme learning machine for regression and classification

时间地点：2013年12月2日下午2：30～4：30， 数计学院6号楼411室

          2013年12月3日上午9：30～11：30，数计学院6号楼411室

          2013年12月4日上午9：30～11：30，数计学院6号楼309室

报告摘要：

It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks.

报告人简介：

Huang Guangbin，1991年在东北大学应用数学专业获得学士学位，1994年在东北大学计算机工程专业获得硕士学位，1999年在新加坡南洋理工大学电子工程专业获得博士学位。目前在新加坡南洋理工大学电子与电气工程学院担任副教授。黄教授长期从事机器学习领域研究，首次在国际上提出了ELM机器学习理论，已发表SCI论文50余篇、会议论文30余篇。目前还担任IEEE Transactions on Cybernetics的副编辑。