Improving neural networks through modularization
Ong, Thian Hock.
Date of Issue2000
School of Electrical and Electronic Engineering
This thesis explores and investigates various possibilities to modularize neural net-works to improve its performance and enhance its capability for pattern classifica-tion and function approximation. We have developed a novel Class-Modularized Neural Network, which utilizes one subnetwork for each class of samples. Where conventional approach generates only the class decision, the CMNN is also capable of providing an indication of the confidence of the class decision. For each class of samples (or subnetwork), it performs the task in two stages: (1) estimation of the class conditional probability density function from the training samples; (2) approximation of the probability density function in (1).
DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Nanyang Technological University