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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3EEQQUB
Repositorysid.inpe.br/sibgrapi/2013/07.12.22.54
Last Update2013:07.12.22.54.34 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2013/07.12.22.54.34
Metadata Last Update2022:06.14.00.07.53 (UTC) administrator
DOI10.1109/SIBGRAPI.2013.13
Citation KeyEscalanteTaubNonaGold:2013:UsUnLe
TitleUsing Unsupervised Learning for Graph Construction in Semi-Supervised Learning with Graphs
FormatOn-line.
Year2013
Access Date2024, Apr. 28
Number of Files1
Size542 KiB
2. Context
Author1 Escalante, Diego Alonso Chávez
2 Taubin, Gabriel
3 Nonato, Luis Gustavo
4 Goldenstein, Siome Klein
Affiliation1 IC-UNICAMP
2 School of Engineering, Brown University
3 ICMC-USP
4 IC-UNICAMP
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
e-Mail Addressddce.2005@gmail.com
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Conference LocationArequipa, Peru
Date5-8 Aug. 2013
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2013-07-12 22:54:34 :: ddce.2005@gmail.com -> administrator ::
2022-06-14 00:07:53 :: administrator -> :: 2013
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsSemi-Supervised Learning
Growing Neural Gas
AbstractSemi-supervised Learning with Graphs can achieve good results in classification tasks even in difficult conditions. Unfortunately, it can be slow and use a lot of memory. The first important step of the graph-based semi-supervised learning approaches is the construction of the graph from the data, where each data-point usually becomes a vertex in the graph a potential problem with large amounts of data. In this paper, we present a graph construction method that uses an unsupervised neural network called growing neural gas (GNG). The GNG instance presents a intelligent stopping criteria that determines when the final network configuration maps correctly the input- data points. With that in mind, we use the final trained network as a reduced input graph for the semi-supervised classification algorithm, associating original data-points to the neurons they have activated in the unsupervised training process.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2013 > Using Unsupervised Learning...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Using Unsupervised Learning...
doc Directory Contentaccess
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3EEQQUB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3EEQQUB
Languageen
Target File114517.pdf
User Groupddce.2005@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SLB4P
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.04.02 9
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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