Metacognitive learning in a fully complex-valued radial basis function neural network
Recent studies on human learning reveal that self-regulated learning in a metacognitive framework is the best strategy for efficient learning. As the machine learning algorithms are inspired by the principles of human learning, one needs to incorporate the concept of metacognition to develop efficie...
Saved in:
Main Authors: | Suresh, Sundaram, Sundararajan, Narasimhan, Savitha, R. |
---|---|
Other Authors: | School of Computer Engineering |
Format: | Article |
Language: | English |
Published: |
2013
|
Online Access: | https://hdl.handle.net/10356/101396 http://hdl.handle.net/10220/11119 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
A fully complex-valued radial basis function classifier for real-valued classification problems
by: Suresh, Sundaram, et al.
Published: (2013) -
A meta-cognitive learning algorithm for a fully complex-valued relaxation network
by: Suresh, Sundaram, et al.
Published: (2013) -
Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems
by: Suresh, Sundaram, et al.
Published: (2013) -
A projection based learning in Meta-cognitive Radial Basis Function Network for classification problems
by: Sateesh Babu, Giduthuri, et al.
Published: (2013) -
Human action recognition using a fast learning fully complex-valued classifier
by: Suresh, Sundaram, et al.
Published: (2013)