Close
Metadata

Reference TypeConference Proceedings
Identifier8JMKD3MGPBW34M/3A3LJ5H
Repositorysid.inpe.br/sibgrapi/2011/07.11.00.34
Metadatasid.inpe.br/sibgrapi/2011/07.11.00.34.07
Sitesibgrapi.sid.inpe.br
Citation KeySilvaCupeZhao:2011:HiLeCl
Author1 Silva, Thiago Christiano
2 Cupertino, Thiago Henrique
3 Zhao, Liang
Affiliation1 Department of Computer Sciences, Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP)
2 Department of Computer Sciences, Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP)
3 Department of Computer Sciences, Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP)
TitleHigh Level Classification for Pattern Recognition
Conference NameConference on Graphics, Patterns and Images, 24 (SIBGRAPI)
Year2011
EditorLewiner, Thomas
Torres, Ricardo
Book TitleProceedings
DateAug. 28 - 31, 2011
Publisher CityLos Alamitos
PublisherIEEE Computer Society Conference Publishing Services
Conference LocationMaceió
Keywordshigh level classification, complex networks.
AbstractTraditional data classification techniques consider only physical features of input data in order to construct their hypotheses. On the other hand, the human (animal) brain performs both low and high order learning and it has facility to identify patterns according to the semantic meaning of input data. In this paper, we propose a data classification technique by combining the low level and the high level learning. The low level term can be implemented by any classification technique, while the high level classification is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies data instances by their physical features, while the latter measures the compliance to the pattern formation of the data. Our study shows that the proposed technique can not only realize classification according to the pattern formation, but it is also able to improve the performance of traditional classification techniques. An application on handwritten digits recognition is performed, revealing that higher classification rates can be obtained when we have a proper mixture of low and high level classifiers.
Languageen
Tertiary TypeFull Paper
FormatDVD, On-line.
Size400 KiB
Number of Files1
Target FileSIBGRAPI2011_Classification.pdf
Last Update2011:07.11.00.34.07 sid.inpe.br/banon/2001/03.30.15.38 thiagoch@icmc.usp.br
Metadata Last Update2011:07.23.15.36.12 sid.inpe.br/banon/2001/03.30.15.38 thiagoch@icmc.usp.br {D 2011}
Document Stagecompleted
Is the master or a copy?is the master
Mirrorsid.inpe.br/banon/2001/03.30.15.38.24
e-Mail Addressthiagoch@icmc.usp.br
User Groupthiagoch@icmc.usp.br
Visibilityshown
Transferable1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Content TypeExternal Contribution
source Directory Contentthere are no files
agreement Directory Content
agreement.html 10/07/2011 21:34 0.5 KiB
History2011-07-23 15:36:12 :: thiagoch@icmc.usp.br -> :: 2011
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
Access Date2019, Dec. 07

Close