Multiclass penalized likelihood pattern classification algorithm

Talaat, AS; Atiya, AF; Mokhtar, SA; Al-Ani, A; Fayek, M

Multiclass penalized likelihood pattern classification algorithm

Talaat, AS Atiya, AF Mokhtar, SA Al-Ani, A

Fayek, M

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7665 LNCS (PART 3), pp. 141 - 148
Issue Date:: 2012-11-19

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Field	Value	Language
dc.contributor.author	Talaat, AS	en_US
dc.contributor.author	Atiya, AF	en_US
dc.contributor.author	Mokhtar, SA	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Fayek, M	en_US
dc.date.issued	2012-11-19	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7665 LNCS (PART 3), pp. 141 - 148	en_US
dc.identifier.isbn	9783642344862	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22873
dc.description.abstract	Penalized likelihood is a general approach whereby an objective function is defined, consisting of the log likelihood of the data minus some term penalizing non-smooth solutions. Subsequently, this objective function is maximized, yielding a solution that achieves some sort of trade-off between the faithfulness and the smoothness of the fit. In this paper we extend the penalized likelihood classification that we proposed in earlier work to the multi class case. The algorithms are based on using a penalty term based on the K-nearest neighbors and the likelihood of the training patterns' classifications. The algorithms are simple to implement, and result in a performance competitive with leading classifiers. © 2012 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-34487-9_18	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Multiclass penalized likelihood pattern classification algorithm	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 3	en_US
utslib.citation.volume	7665 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Doha, Qatar,	en_US
pubs.embargo.period	Not known	en_US
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	PART 3	en_US
pubs.publication-status	Published	en_US
pubs.volume	7665 LNCS	en_US

Abstract:

Penalized likelihood is a general approach whereby an objective function is defined, consisting of the log likelihood of the data minus some term penalizing non-smooth solutions. Subsequently, this objective function is maximized, yielding a solution that achieves some sort of trade-off between the faithfulness and the smoothness of the fit. In this paper we extend the penalized likelihood classification that we proposed in earlier work to the multi class case. The algorithms are based on using a penalty term based on the K-nearest neighbors and the likelihood of the training patterns' classifications. The algorithms are simple to implement, and result in a performance competitive with leading classifiers. © 2012 Springer-Verlag.

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http://hdl.handle.net/10453/22873