Semi-supervised structuring of complex data

Rizoiu, MA

Semi-supervised structuring of complex data

Rizoiu, MA

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Publisher:: IJCAI/AAAI
Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2013, pp. 3239-3240
Issue Date:: 2013-12-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Rizoiu, MA
dc.contributor.editor	Rossi, F
dc.date.accessioned	2021-01-22T06:33:37Z
dc.date.available	2021-01-22T06:33:37Z
dc.date.issued	2013-12-01
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2013, pp. 3239-3240
dc.identifier.isbn	9781577356332
dc.identifier.issn	1045-0823
dc.identifier.uri	http://hdl.handle.net/10453/145560
dc.description.abstract	The objective of the thesis is to explore how complex data can be treated using unsupervised machine learning techniques, in which additional information is injected to guide the exploratory process. Starting from specific problems, our contributions take into account the different dimensions of the complex data: their nature (image, text), the additional information attached to the data (labels, structure, concept ontologies) and the temporal dimension. A special attention is given to data representation and how additional information can be leveraged to improve this representation.
dc.language	en
dc.publisher	IJCAI/AAAI
dc.relation.ispartof	IJCAI International Joint Conference on Artificial Intelligence
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Semi-supervised structuring of complex data
dc.type	Conference Proceeding
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/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2021-01-22T06:33:14Z
pubs.publication-status	Published

Abstract:

The objective of the thesis is to explore how complex data can be treated using unsupervised machine learning techniques, in which additional information is injected to guide the exploratory process. Starting from specific problems, our contributions take into account the different dimensions of the complex data: their nature (image, text), the additional information attached to the data (labels, structure, concept ontologies) and the temporal dimension. A special attention is given to data representation and how additional information can be leveraged to improve this representation.

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http://hdl.handle.net/10453/145560