Extreme Learning Machine: Towards Human Brain Alike Learning
Neural networks (NN) and support vector machines (SVM) play key roles in machine learning and data analysis in the past 2-3 decades. However, it is known that these popular learning techniques face some challenging issues such as: intensive human intervene, slow learning speed, poor learning scalability. This talk will introduce a next generation of learning theory; the resultant biologically inspired learning technique referred to as Extreme Learning Machine (ELM) and its wide applications. ELM not only learns up to tens of thousands faster than NN and SVMs, but also provides unified implementation for regression, binary and multi-class applications. ELM not only produces good results for sparse datasets but also is efficient for large size of applications. From both theoretical and practical points of view, NN and SVM/LS-SVM may only produce suboptimal solutions to ELM. Our preliminary study also shows that ELM outperforms Deep Learning in both learning accuracy and learning speed (up to tens of thousands times faster). ELM is efficient in time series, online sequential, incremental applications. More and more researchers are studying ELM and its potential applications in face recognition, EEG signal processing, brain computer interface, medical image processing, bioinformatics, disease prediction/detection, object recognition, knowledge discovery, data privacy, security, image quality assessment, semantic web, hardware implementation, cloud computing, and many other industrial applications.
Prof. Guang-Bin HUANG
Date & Time
5 Dec 2013 (Thursday) 11:00 - 12:30
J317 (University of Macau)
Department of Computer and Information Science
Guang-Bin Huang serves as an Associate Editor of Neurocomputing, Cognitive Computation, neural networks, and IEEE Transactions on Systems, Man and Cybernetics - Part B. He is a senior member of IEEE. His SCI is 2600+. One of his main works is to propose a new machine learning theory and learning techniques called Extreme Learning Machines (ELM).
His current research interests include big data analytics, human computer interface, brain computer interface, image processing/understanding, machine learning theories and algorithms, extreme learning machine, and pattern recognition. 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, Singapore. He is program leader of BMW-NTU Joint Future Mobility Lab on Human Machine Interface and Assisted Driving. He has led/implemented several key industrial projects (e.g., Chief architect/designer and technical leader of Singapore Changi Airport Cargo Terminal 5 Inventory Control System (T5 ICS) Upgrading Project, etc).
From June 1998 to May 2001, he worked as a research fellow in Singapore Institute of Manufacturing Technology. He was the founder of two laboratories (mainly on remote video monitoring, social networking, video conference, IP phone, and distance learning, etc) and led several significant industrial projects.