Incremental extreme learning machine
This new theory shows that in order to let SLFNs work as universal approximators, one may simply randomly choose input-to-hidden nodes, and then we only need to adjust the output weights linking the hidden layer and the output layer. In such SLFNs implementations, the activation functions for additi...
Saved in:
Main Author: | Chen, Lei |
---|---|
Other Authors: | Huang Guangbin |
Format: | Theses and Dissertations |
Published: |
2008
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/3804 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Similar Items
-
Extreme learning machines
by: Zhu, Qinyu
Published: (2008) -
Sequential learning for extreme learning machine
by: Liang, Nanying
Published: (2008) -
Extreme learning machine (ELM) methods for pedestrian detection
by: Song, Qiaozhi
Published: (2016) -
Extreme learning machine based speaker recognition
by: Hu, Zongjiang.
Published: (2011) -
Extreme learning machine based financial prediction
by: Liu, Yishan.
Published: (2012)