Applying Machine Learning for Automated Classification of Biomedical Data in Subject-independent Settings

Pham, TT

Applying Machine Learning for Automated Classification of Biomedical Data in Subject-independent Settings

Pham, TT

Permalink

Publisher:: Springer
Publication Type:: Book
Citation:: 2018, pp. 1-107
Issue Date:: 2018

Closed Access

	Filename	Description	Size
	2019_Bookmatter_ApplyingMachineLearningForAuto.pdf	Published version	195.44 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Pham, TT
dc.date.accessioned	2021-10-17T21:09:50Z
dc.date.available	2021-10-17T21:09:50Z
dc.date.issued	2018
dc.identifier.citation	2018, pp. 1-107
dc.identifier.isbn	3319986759
dc.identifier.isbn	9783319986753
dc.identifier.uri	http://hdl.handle.net/10453/151128
dc.description.abstract	Nominated as an outstanding PhD thesis by The University of Sydney, Australia Reports on an improved feature selection technique based on voting Offers a comprehensive review of machine learning methods for unsupervised classification and feature selection
dc.language	en
dc.publisher	Springer
dc.relation.isbasedon	10.1007/978-3-319-98675-3
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Applying Machine Learning for Automated Classification of Biomedical Data in Subject-independent Settings
dc.type	Book
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-10-17T21:09:47Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
dc.location	Switzerland

Abstract:

Nominated as an outstanding PhD thesis by The University of Sydney, Australia Reports on an improved feature selection technique based on voting Offers a comprehensive review of machine learning methods for unsupervised classification and feature selection

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/151128