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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/39TRMQ5
Repositorysid.inpe.br/sibgrapi/2011/06.23.17.47
Last Update2011:06.23.17.47.44 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2011/06.23.17.47.44
Metadata Last Update2022:06.14.00.07.06 (UTC) administrator
DOI10.1109/SIBGRAPI.2011.35
Citation KeySilvaCupeZhao:2011:StCoLe
TitleStochastic Competitive Learning Applied to Handwritten Digit and Letter Clustering
FormatDVD, On-line.
Year2011
Access Date2024, Apr. 28
Number of Files1
Size589 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 00:07:06 :: administrator -> :: 2011
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsstochastic competitive learning
handwritten pattern clustering
AbstractCompetitive learning is an important mechanism for data clustering and pattern recognition. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large scale networks. In this model, several particles walk in the network and compete with each other to occupy as many nodes as possible, while attempting to reject intruder particles. As a result, each particle will dominate a cluster of the network. Moreover, we propose an efficient method for determining the right number of clusters by using the information generated by the competition process itself, avoiding the calculation of an external evaluating index. In this work, we apply the model to handwritten data clustering. Computer simulations reveal that the proposed technique obtains satisfactory cluster detection accuracy.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2011 > Stochastic Competitive Learning...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Stochastic Competitive Learning...
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/39TRMQ5
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/39TRMQ5
Languageen
Target FileSIBGRAPI2011_ParticleCompetition.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 3
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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