Please use this identifier to cite or link to this item:
|Title:||Report on industrial attachment with DSO National laboratories on computational intelligence for knowledge discovery||Authors:||Kok, Hong Jie||Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2014||Abstract:||A Bayesian network is a graph which features conditional probability tables as edges, and variables or events as nodes. This network is a Directed Acyclic Graph the structure reflects the dependencies of the nodes. There are several algorithms available to learn a Bayesian network, and the focus here is on latent tree learning algorithms which can discover structures with hidden nodes, which may reflect simpler relationships and better categorization of data. By integrating these algorithms into an existing learning knowledge system, the evaluation of performance in terms of structure scoring metrics and classification accuracy can be carried out to compare the effectiveness of these algorithms to those traditional learning methods.||URI:||http://hdl.handle.net/10356/65252||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.