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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3A3LJ5H
Repositorysid.inpe.br/sibgrapi/2011/07.11.00.34
Last Update2011:07.11.00.34.08 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2011/07.11.00.34.07
Metadata Last Update2022:06.14.00.07.17 (UTC) administrator
DOI10.1109/SIBGRAPI.2011.19
Citation KeySilvaCupeZhao:2011:HiLeCl
TitleHigh Level Classification for Pattern Recognition
FormatDVD, On-line.
Year2011
Access Date2024, Apr. 29
Number of Files1
Size400 KiB
2. Context
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)
EditorLewiner, Thomas
Torres, Ricardo
e-Mail Addressthiagoch@icmc.usp.br
Conference NameConference on Graphics, Patterns and Images, 24 (SIBGRAPI)
Conference LocationMaceió, AL, Brazil
Date28-31 Aug. 2011
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2011-07-23 15:36:12 :: thiagoch@icmc.usp.br -> administrator :: 2011
2022-06-14 00:07:17 :: administrator -> :: 2011
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
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.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2011 > High Level Classification...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > High Level Classification...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3A3LJ5H
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3A3LJ5H
Languageen
Target FileSIBGRAPI2011_Classification.pdf
User Groupthiagoch@icmc.usp.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SKNPE
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.00.56 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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