Extreme Learning Machine: Why Tuning Is Not Required in Learning?

June 7, 2011
Guang-Bin Huang | School of Electrical & Electronic Engineering

Neural networks (NN) and support vector machines (SVM) play key roles in machine learning and data analysis. However, it is known that these popular learning techniques face some challenging issues such as: intensive human intervene, slow learning speed, poor learning scalability. The newly proposed Extreme Learning Machine (ELM) can resolve those challenging issues. This talk will give the answers on the reasons: 1) why ELM can work as universal approximator and why tuning is not required; 2) why ELM can differentiate any disjoint classification regions; and 3) why ELM outperforms LS-SVM. This talk will also introduce an efficient incremental implementation of ELM and discuss some open problems.

Speaker Details

Guang-Bin Huang received the B.Sc degree in applied mathematics and M.Eng degree in computer engineering from Northeastern University, P. R. China, in 1991 and 1994, respectively, and Ph.D degree in electrical engineering from Nanyang Technological University, Singapore in 1999. During undergraduate period, he also concurrently studied in Applied Mathematics department and Wireless Communication department of Northeastern University, P. R. China.

From June 1998 to May 2001, he worked as Research Fellow in Singapore Institute of Manufacturing Technology (formerly known as Gintic Institute of Manufacturing Technology) where he has led/implemented several key industrial projects (e.g., Chief designer and technical leader of Singapore Changi Airport Cargo Terminal Upgrading Project, etc). He has extensive experiences in the applications of software engineering, computer networks, communications, and manufacturing. From May 2001, he has been working as an Assistant Professor and Associate Professor (with tenure) in the School of Electrical and Electronic Engineering, Nanyang Technological University. His current research interests include machine learning, computational intelligence, extreme learning machine, pattern recognition, games, and remanufacturing. He serves as an Associate Editor of Neurocomputing and IEEE Transactions on Systems, Man and Cybernetics.