dc.contributor.authorFeng, Chu
dc.date.accessioned2010-06-16T04:52:25Z
dc.date.accessioned2017-07-23T08:33:01Z
dc.date.available2010-06-16T04:52:25Z
dc.date.available2017-07-23T08:33:01Z
dc.date.copyright2008en_US
dc.date.issued2008
dc.identifier.citationFeng, C. (2008). Mining minimal discriminatice features sets and its applications to gene expression data analysis. Doctoral thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/40532
dc.description.abstractFeature subset selection has been an important problem in machine learning research. Recently, new appeared data with high dimensionality, such as microarray gene expression data and text classification data, drive feature subset selection techniques advance speedily.en_US
dc.format.extent170 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleMining minimal discriminatice features sets and its applications to gene expression data analysisen_US
dc.typeThesis
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.supervisorWang Lipo
dc.description.degreeDOCTOR OF PHILOSOPHY (EEE)en_US
dc.identifier.doihttps://doi.org/10.32657/10356/40532


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