Report on industrial attachment with DSO National laboratories on computational intelligence for knowledge discovery

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...

Full description

Saved in:
Bibliographic Details
Main Author: Kok, Hong Jie
Other Authors: Sui Qing
Format: Industrial Attachment (IA)
Language:English
Published: 2015
Subjects:
Online Access:http://hdl.handle.net/10356/65252
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-65252
record_format dspace
spelling sg-ntu-dr.10356-652522023-07-07T15:48:00Z Report on industrial attachment with DSO National laboratories on computational intelligence for knowledge discovery Kok, Hong Jie Sui Qing School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering 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. 0 2015-06-17T05:52:59Z 2015-06-17T05:52:59Z 2014 2014 Industrial Attachment (IA) http://hdl.handle.net/10356/65252 en Nanyang Technological University 22 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Kok, Hong Jie
Report on industrial attachment with DSO National laboratories on computational intelligence for knowledge discovery
description 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.
author2 Sui Qing
author_facet Sui Qing
Kok, Hong Jie
format Industrial Attachment (IA)
author Kok, Hong Jie
author_sort Kok, Hong Jie
title Report on industrial attachment with DSO National laboratories on computational intelligence for knowledge discovery
title_short Report on industrial attachment with DSO National laboratories on computational intelligence for knowledge discovery
title_full Report on industrial attachment with DSO National laboratories on computational intelligence for knowledge discovery
title_fullStr Report on industrial attachment with DSO National laboratories on computational intelligence for knowledge discovery
title_full_unstemmed Report on industrial attachment with DSO National laboratories on computational intelligence for knowledge discovery
title_sort report on industrial attachment with dso national laboratories on computational intelligence for knowledge discovery
publishDate 2015
url http://hdl.handle.net/10356/65252
_version_ 1772826106202685440